Local Wavelet Pattern: A New Feature Descriptor for Image Retrieval in Medical CT Databases

CLIP in medical imaging: A survey

Contrastive Language-Image Pre-training (CLIP), a simple yet effective pre-training paradigm, successfully introduces text supervision to vision models. It has shown promising results across various tasks due to its generalizability and interpretability. The use of CLIP has recently gained increasing interest in the medical imaging domain, serving as a pre-training paradigm for image-text alignment, or a critical component in diverse clinical tasks. With the aim of facilitating a deeper understanding of this promising direction, this survey offers an in-depth exploration of the CLIP within the domain of medical imaging, regarding both refined CLIP pre-training and CLIP-driven applications. In this paper, we (1) first start with a brief introduction to the fundamentals of CLIP methodology; (2) then investigate the adaptation of CLIP pre-training in the medical imaging domain, focusing on how to optimize CLIP given characteristics of medical images and reports; (3) further explore practical utilization of CLIP pre-trained models in various tasks, including classification, dense prediction, and cross-modal tasks; and (4) finally discuss existing limitations of CLIP in the context of medical imaging, and propose forward-looking directions to address the demands of medical imaging domain. Studies featuring technical and practical value are both investigated. We expect this survey will provide researchers with a holistic understanding of the CLIP paradigm and its potential implications. The project page of this survey can also be found on Github.

A New Approach for Effective Retrieval of Medical Images: A Step towards Computer-Assisted Diagnosis

The biomedical imaging field has grown enormously in the past decade. In the era of digitization, the demand for computer-assisted diagnosis is increasing day by day. The COVID-19 pandemic further emphasized how retrieving meaningful information from medical repositories can aid in improving the quality of patient's diagnosis. Therefore, content-based retrieval of medical images has a very prominent role in fulfilling our ultimate goal of developing automated computer-assisted diagnosis systems. Therefore, this paper presents a content-based medical image retrieval system that extracts multi-resolution, noise-resistant, rotation-invariant texture features in the form of a novel pattern descriptor, i.e., MsNrRiTxP, from medical images. In the proposed approach, the input medical image is initially decomposed into three neutrosophic images on its transformation into the neutrosophic domain. Afterwards, three distinct pattern descriptors, i.e., MsTrP, NrTxP, and RiTxP, are derived at multiple scales from the three neutrosophic images. The proposed MsNrRiTxP pattern descriptor is obtained by scale-wise concatenation of the joint histograms of MsTrP×RiTxP and NrTxP×RiTxP. To demonstrate the efficacy of the proposed system, medical images of different modalities, i.e., CT and MRI, from four test datasets are considered in our experimental setup. The retrieval performance of the proposed approach is exhaustively compared with several existing, recent, and state-of-the-art local binary pattern-based variants. The retrieval rates obtained by the proposed approach for the noise-free and noisy variants of the test datasets are observed to be substantially higher than the compared ones.

RbQE: An Efficient Method for Content-Based Medical Image Retrieval Based on Query Expansion

Systems for retrieving and managing content-based medical images are becoming more important, especially as medical imaging technology advances and the medical image database grows. In addition, these systems can also use medical images to better grasp and gain a deeper understanding of the causes and treatments of different diseases, not just for diagnostic purposes. For achieving all these purposes, there is a critical need for an efficient and accurate content-based medical image retrieval (CBMIR) method. This paper proposes an efficient method (RbQE) for the retrieval of computed tomography (CT) and magnetic resonance (MR) images. RbQE is based on expanding the features of querying and exploiting the pre-trained learning models AlexNet and VGG-19 to extract compact, deep, and high-level features from medical images. There are two searching procedures in RbQE: a rapid search and a final search. In the rapid search, the original query is expanded by retrieving the top-ranked images from each class and is used to reformulate the query by calculating the mean values for deep features of the top-ranked images, resulting in a new query for each class. In the final search, the new query that is most similar to the original query will be used for retrieval from the database. The performance of the proposed method has been compared to state-of-the-art methods on four publicly available standard databases, namely, TCIA-CT, EXACT09-CT, NEMA-CT, and OASIS-MRI. Experimental results show that the proposed method exceeds the compared methods by 0.84%, 4.86%, 1.24%, and 14.34% in average retrieval precision (ARP) for the TCIA-CT, EXACT09-CT, NEMA-CT, and OASIS-MRI databases, respectively.

PCA-Based Advanced Local Octa-Directional Pattern (ALODP-PCA): A Texture Feature Descriptor for Image Retrieval

This paper presents a novel feature descriptor termed principal component analysis (PCA)-based Advanced Local Octa-Directional Pattern (ALODP-PCA) for content-based image retrieval. The conventional approaches compare each pixel of an image with certain neighboring pixels providing discrete image information. The descriptor proposed in this work utilizes the local intensity of pixels in all eight directions of its neighborhood. The local octa-directional pattern results in two patterns, i.e., magnitude and directional, and each is quantized into a 40-bin histogram. A joint histogram is created by concatenating directional and magnitude histograms. To measure similarities between images, the Manhattan distance is used. Moreover, to maintain the computational cost, PCA is applied, which reduces the dimensionality. The proposed methodology is tested on a subset of a Multi-PIE face dataset. The dataset contains almost 800,000 images of over 300 people. These images carries different poses and have a wide range of facial expressions. Results were compared with state-of-the-art local patterns, namely, the local tri-directional pattern (LTriDP), local tetra directional pattern (LTetDP), and local ternary pattern (LTP). The results of the proposed model supersede the work of previously defined work in terms of precision, accuracy, and recall.

Local bit-plane neighbour dissimilarity pattern in non-subsampled shearlet transform domain for bio-medical image retrieval

This paper introduces a novel descriptor non-subsampled shearlet transform (NSST) local bit-plane neighbour dissimilarity pattern (NSST-LBNDP) for biomedical image retrieval based on NSST, bit-plane slicing and local pattern based features. In NSST-LBNDP, the input image is first decomposed by NSST, followed by introduction of non-linearity on the NSST coefficients by computing local energy features. The local energy features are next normalized into 8-bit values. The multiscale NSST is used to provide translational invariance and has flexible directional sensitivity to catch more anisotropic information of an image. The normalised NSST subband features are next decomposed into bit-plane slices in order to capture very fine to coarse subband details. Then each bit-plane slices of all the subbands are encoded by exploiting the dissimilarity relationship between each neighbouring pixel and its adjacent neighbours. Experiments on two computed tomography (CT) and one magnetic resonance imaging (MRI) image datasets confirms the superior results of NSST-LBNDP when compared to many recent well known relevant descriptors both in terms of average retrieval precision (ARP) and average retrieval recall (ARR).

COVID-19–affected medical image analysis using DenserNet

The COrona VIrus Disease (COVID-19) outbreak has been announced as a pandemic by the World Health Organization (WHO) in mid-February 2020. With the current pandemic situation, the testing and detection of this disease are becoming a challenge in many regions across the globe because of the insufficiency of the suitable testing infrastructure. The shortage of kits to test COVID-19 has led to another crisis owing to worldwide supply-demand mismatch, and thereby, widen up a new research area that deals with the detection of COVID-19 without the test kit. In this paper, we investigate medical images, mostly chest X-ray images and thorax computed tomography (CT) scans to identify the attack of COVID-19. In countries, where the number of medical experts is lesser than the expected as recommended by WHO, this computer-aided system can be useful as it requires minimal human intervention. Consequently, this technology reduces the chances of contagious infection. This study may further help in the early detection of people with some similar symptoms of coronavirus. Early detection and intervention can play a pivotal role in coronavirus treatment. The primary goal of our work is to detect COVID-19–affected cases. However, this work can be extended to detect pneumonia because of Severe Acute Respiratory Syndrome, Acute Respiratory Distress Syndrome, Middle East Respiratory Syndrome, and bacteria-like Streptococcus. In this paper, we employ publicly available medical images obtained from various demographics, and propose a rapid cost-effective test leveraging a deep learning-based framework. Here, we propose a new architecture based on a densely connected convolutional neural network to analyze the COVID-19–affected medical images. We name our proposed architecture as DenserNet, which is an improvisation of DenseNet. Our proposed Denser Net architecture achieved 96.18% and 87.19% accuracies on two publicly available databases containing chest X-ray images and thorax CT scans, respectively, for the task of separating COVID-19 and non-COVID-19 images, which is quite encouraging.

3D-local oriented zigzag ternary co-occurrence fused pattern for biomedical CT image retrieval

In this letter, a new feature descriptor called three dimensional local oriented zigzag ternary co-occurrence fused pattern ( 3 D - L O Z T C o F P ) is proposed for computed tomography (CT) image retrieval. Unlike the conventional local pattern based approaches, where the relationship between the reference and its neighbors in a circular shaped neighborhood are captured in a 2-D plane, the proposed descriptor encodes the relationship between the reference and it's neighbors within a local 3D block drawn from multiscale Gaussian filtered images employing a new 3D zigzag sampling structure. The proposed 3D zigzag scan around a reference not only provides an effective texture representation by capturing non-uniform and uniform local texture patterns but the fine to coarse details are also captured via multiscale Gaussian filtered images. In this letter, we have introduced three unique 3D zigzag patterns in four diverse directions. In 3 D - L O Z T C o F P , we first calculate the 3D local ternary pattern within a local 3D block around a reference using proposed 3D zigzag sampling structure at both radius 1 and 2. Then the co-occurrence of similar ternary edges within the local 3D cube is computed to further enhance the discriminative power of the descriptor. A quantization and fusion based scheme is introduced to reduce the feature dimension of the proposed descriptor. Experiments are conducted on popular NEMA and TCIA-CT image databases and the results demonstrate superior retrieval efficiency of the proposed 3 D - L O Z T C o F P descriptor over many local pattern based approaches in terms of average retrieval precision and average retrieval recall in CT image retrieval.

An Efficient Content-Based Image Retrieval System for the Diagnosis of Lung Diseases

The main problem in content-based image retrieval (CBIR) systems is the semantic gap which needs to be reduced for efficient retrieval. The common imaging signs (CISs) which appear in the patient's lung CT scan play a significant role in the identification of cancerous lung nodules and many other lung diseases. In this paper, we propose a new combination of descriptors for the effective retrieval of these imaging signs. First, we construct a feature database by combining local ternary pattern (LTP), local phase quantization (LPQ), and discrete wavelet transform. Next, joint mutual information (JMI)-based feature selection is deployed to reduce the redundancy and to select an optimal feature set for CISs retrieval. To this end, similarity measurement is performed by combining visual and semantic information in equal proportion to construct a balanced graph and the shortest path is computed for learning contextual similarity to obtain final similarity between each query and database image. The proposed system is evaluated on a publicly available database of lung CT imaging signs (LISS), and results are retrieved based on visual feature similarity comparison and graph-based similarity comparison. The proposed system achieves a mean average precision (MAP) of 60% and 0.48 AUC of precision-recall (P-R) graph using only visual features similarity comparison. These results further improve on graph-based similarity measure with a MAP of 70% and 0.58 AUC which shows the superiority of our proposed scheme.

A Shape-Based Statistical Inversion Method for EIT/URT Dual-Modality Imaging

A shape-based statistical inversion method is proposed for Electrical Impedance Tomography (EIT) and Ultrasound Reflection Tomography (URT) dual-modality imaging. It is promising to improve the imaging accuracy in inclusion detection problems. The proposed image reconstruction method is based on the statistical shape inversion framework. The likelihood function is derived from EIT and URT forward models. The prior distribution is constructed using the Markov random field (MRF) prior. The measurement uncertainty is modeled by conditional error model method. The statistical shape inversion problem is solved by the Maximum a posterior (MAP) method with conventional error model. A set of numerical and experimental tests are carried out to evaluate the performance of the proposed method. The results show that the proposed EIT/URT dual-modality imaging method has obvious improvement in imaging accuracy compared to the traditional single-modality EIT and URT methods.

A multi-feature image retrieval scheme for pulmonary nodule diagnosis

Deep analysis of radiographic images can quantify the extent of intra-tumoral heterogeneity for personalized medicine.In this paper, we propose a novel content-based multi-feature image retrieval (CBMFIR) scheme to discriminate pulmonary nodules benign or malignant. Two types of features are applied to represent the pulmonary nodules. With each type of features, a single-feature distance metric model is proposed to measure the similarity of pulmonary nodules. And then, multiple single-feature distance metric models learned from different types of features are combined to a multi-feature distance metric model. Finally, the learned multi-feature distance metric is used to construct a content-based image retrieval (CBIR) scheme to assist the doctors in diagnosis of pulmonary nodules. The classification accuracy and retrieval accuracy are used to evaluate the performance of the scheme.The classification accuracy is 0.955 ± 0.010, and the retrieval accuracies outperform the comparison methods.The proposed CBMFIR scheme is effective in diagnosis of pulmonary nodules. Our method can better integrate multiple types of features from pulmonary nodules.

Quality of Service aware Medical CT Image Transmission Anti-collision Mechanism Based on Big Data Autonomous Anti-collision Control

Background:

At present, due to the limitation of hardware, software and network transmission performance, the medical diagnosis of medical CT image equipment is easy to be carried out based on the wrong image. In addition, due to the complex structure of human organs and unpredictable lesion location, it is difficult to judge the reliability of medical CT images, spatial localization of the lesion, two-dimensional slice images and shape based on stereotypes. Therefore, how to improve the efficiency of medical CT terminal and the image quality has become the key technology to improve the satisfaction of medical diagnosis and treatment.

Objective:

To improve the work efficiency of medical CT terminal and medical image transmission quality, with the medical CT terminal state and service quality.

Methods:

Firstly, from the view of throughput, packet loss rate, delay and so on, a QoS aware model for medical CT image transmission has been established. Then, with throughput, packet length, path loss, service area size, access point location, and the number of medical CT terminals, the performance change regulation of the medical CT image transmission is completed and the optimal quality of service guarantee parameters sequence is obtained. Next, the medical CT image big data autonomous collision control scheme is proposed.

Results:

The experimental and mathematical results verify the real-time performance, reliability, effectiveness and feasibility of the proposed medical CT image transmission anti-collision mechanism.

Conclusion:

The proposed scheme can satisfy the high-quality high demand for data transmission at the same time, according to a variety of user experience demand and real-time adjustment of medical CT terminal working state, which provides effective data quality assurance and optimization of the network source distribution, and also enhances the quality of medical image data transmission service.

Exploring the Utilization of Gradient Information in SIFT Based Local Image Descriptors

The utilization of gradient information is a key issue in building Scale Invariant Feature Transform (SIFT)-like descriptors. In the literature, two types of gradient information, i.e., Gradient Magnitude (GM) and Gradient Occurrence (GO), are used for building descriptors. However, both of these two types of gradient information have limitations in building and matching local image descriptors. In our prior work, a strategy of combining these two types of gradient information was proposed to intersect the keypoint matches which are obtained by using gradient magnitude and gradient occurrence individually. Different from this combination strategy, this paper explores novel strategies of weighting these two types of gradient information to build new descriptors with high discriminative power. These proposed weighting strategies are extensively evaluated against gradient magnitude and gradient occurrence as well as the combination strategy on a few image registration datasets. From the perspective of building new descriptors, experimental results will show that each of the proposed strategies achieve higher matching accuracy as compared to both GM-based and GO-based descriptors. In terms of recall results, one of the proposed strategies outperforms both GM-based and GO-based descriptors.

Machine learning for computer-aided polyp detection using wavelets and content-based image

The continuous growing of machine learning techniques, their capabilities improvements and the availability of data being continuously collected, recorded and updated, can enhance diagnosis stages by making it faster and more accurate than human diagnosis. In lower endoscopies procedures, most of the diagnosis relies on the capabilities and expertise of the physician. During medical training, physicians can be benefited from the assistance of algorithms able to automatically detect polyps, thus enhancing their diagnosis. In this paper, we propose a machine learning approach trained to detect polyps in lower endoscopies recordings with high accuracy and sensitivity, previously processed using wavelet transform for feature extraction. The propose system is validated using available datasets. From a set of 1132 images, our system showed a 97.9% of accuracy in diagnosing polyps, around 10% more efficient than other approaches using techniques with a low computational requirement previously published. In addition, the false positive rate was 0.03. This encouraging result can be also extended to other diagnosis.

Content based medical image retrieval based on new efficient local neighborhood wavelet feature descriptor

This paper presents a new class of local neighborhood based wavelet feature descriptor (LNWFD) for content based medical image retrieval (CBMIR). To retrieve images effectively from large medical databases is backbone of diagnosis. Existing wavelet transform based medical image retrieval methods suffer from high length feature vector with confined retrieval performance. Triplet half-band filter bank (THFB) enhanced the properties of wavelet filters using three kernels. The influence of THFB has employed in the proposed method. First, triplet half-band filter bank (THFB) is used for single level wavelet decomposition to obtain four sub-bands. Next, the relationship among wavelet coefficients is exploited at each sub-band using 3 × 3 neighborhood window to form LNWFD pattern. The novelty of the proposed descriptor lies in exploring relation between wavelet transform values of pixels rather than intensity values which gives more detail local information in wavelet sub-bands. Thus, proposed feature descriptor is robust against illumination. Manhattan distance is used to compute similarity between query feature vector and feature vector of database. The proposed method is tested for medical image retrieval using OASIS-MRI, NEMA-CT, and Emphysema-CT databases. The average retrieval precisions achieved are 71.45%, 99.51% of OASIS-MRI and NEMA-CT databases for top ten matches considered respectively and 55.51% of Emphysema-CT database for top 50 matches. The superiority in terms of performance of the proposed method is confirmed by the experimental results over the well-known existing descriptors.

Radiological images and machine learning: Trends, perspectives, and prospects

The application of machine learning to radiological images is an increasingly active research area that is expected to grow in the next five to ten years. Recent advances in machine learning have the potential to recognize and classify complex patterns from different radiological imaging modalities such as x-rays, computed tomography, magnetic resonance imaging and positron emission tomography imaging. In many applications, machine learning based systems have shown comparable performance to human decision-making. The applications of machine learning are the key ingredients of future clinical decision making and monitoring systems. This review covers the fundamental concepts behind various machine learning techniques and their applications in several radiological imaging areas, such as medical image segmentation, brain function studies and neurological disease diagnosis, as well as computer-aided systems, image registration, and content-based image retrieval systems. Synchronistically, we will briefly discuss current challenges and future directions regarding the application of machine learning in radiological imaging. By giving insight on how take advantage of machine learning powered applications, we expect that clinicians can prevent and diagnose diseases more accurately and efficiently.

A novel biomedical image indexing and retrieval system via deep preference learning

BACKGROUND AND OBJECTIVES

The traditional biomedical image retrieval methods as well as content-based image retrieval (CBIR) methods originally designed for non-biomedical images either only consider using pixel and low-level features to describe an image or use deep features to describe images but still leave a lot of room for improving both accuracy and efficiency. In this work, we propose a new approach, which exploits deep learning technology to extract the high-level and compact features from biomedical images. The deep feature extraction process leverages multiple hidden layers to capture substantial feature structures of high-resolution images and represent them at different levels of abstraction, leading to an improved performance for indexing and retrieval of biomedical images.

METHODS

We exploit the current popular and multi-layered deep neural networks, namely, stacked denoising autoencoders (SDAE) and convolutional neural networks (CNN) to represent the discriminative features of biomedical images by transferring the feature representations and parameters of pre-trained deep neural networks from another domain. Moreover, in order to index all the images for finding the similarly referenced images, we also introduce preference learning technology to train and learn a kind of a preference model for the query image, which can output the similarity ranking list of images from a biomedical image database. To the best of our knowledge, this paper introduces preference learning technology for the first time into biomedical image retrieval.

RESULTS

We evaluate the performance of two powerful algorithms based on our proposed system and compare them with those of popular biomedical image indexing approaches and existing regular image retrieval methods with detailed experiments over several well-known public biomedical image databases. Based on different criteria for the evaluation of retrieval performance, experimental results demonstrate that our proposed algorithms outperform the state-of-the-art techniques in indexing biomedical images.

CONCLUSIONS

We propose a novel and automated indexing system based on deep preference learning to characterize biomedical images for developing computer aided diagnosis (CAD) systems in healthcare. Our proposed system shows an outstanding indexing ability and high efficiency for biomedical image retrieval applications and it can be used to collect and annotate the high-resolution images in a biomedical database for further biomedical image research and applications.

Content-based image retrieval for Lung Nodule Classification Using Texture Features and Learned Distance Metric

Similarity measurement of lung nodules is a critical component in content-based image retrieval (CBIR), which can be useful in differentiating between benign and malignant lung nodules on computer tomography (CT). This paper proposes a new two-step CBIR scheme (TSCBIR) for computer-aided diagnosis of lung nodules. Two similarity metrics, semantic relevance and visual similarity, are introduced to measure the similarity of different nodules. The first step is to search for K most similar reference ROIs for each queried ROI with the semantic relevance metric. The second step is to weight each retrieved ROI based on its visual similarity to the queried ROI. The probability is computed to predict the likelihood of the queried ROI depicting a malignant lesion. In order to verify the feasibility of the proposed algorithm, a lung nodule dataset including 366 nodule regions of interest (ROIs) is assembled from LIDC-IDRI lung images on CT scans. Three groups of texture features are implemented to represent a nodule ROI. Our experimental results on the assembled lung nodule dataset show good performance improvement over existing popular classifiers.

Selective Convolutional Descriptor Aggregation for Fine-Grained Image Retrieval

Deep convolutional neural network models pre-trained for the ImageNet classification task have been successfully adopted to tasks in other domains, such as texture description and object proposal generation, but these tasks require annotations for images in the new domain. In this paper, we focus on a novel and challenging task in the pure unsupervised setting: fine-grained image retrieval. Even with image labels, fine-grained images are difficult to classify, letting alone the unsupervised retrieval task. We propose the selective convolutional descriptor aggregation (SCDA) method. The SCDA first localizes the main object in fine-grained images, a step that discards the noisy background and keeps useful deep descriptors. The selected descriptors are then aggregated and the dimensionality is reduced into a short feature vector using the best practices we found. The SCDA is unsupervised, using no image label or bounding box annotation. Experiments on six fine-grained data sets confirm the effectiveness of the SCDA for fine-grained image retrieval. Besides, visualization of the SCDA features shows that they correspond to visual attributes (even subtle ones), which might explain SCDA's high-mean average precision in fine-grained retrieval. Moreover, on general image retrieval data sets, the SCDA achieves comparable retrieval results with the state-of-the-art general image retrieval approaches.

A novel end-to-end classifier using domain transferred deep convolutional neural networks for biomedical images

BACKGROUND AND OBJECTIVES

Highly accurate classification of biomedical images is an essential task in the clinical diagnosis of numerous medical diseases identified from those images. Traditional image classification methods combined with hand-crafted image feature descriptors and various classifiers are not able to effectively improve the accuracy rate and meet the high requirements of classification of biomedical images. The same also holds true for artificial neural network models directly trained with limited biomedical images used as training data or directly used as a black box to extract the deep features based on another distant dataset. In this study, we propose a highly reliable and accurate end-to-end classifier for all kinds of biomedical images via deep learning and transfer learning.

METHODS

We first apply domain transferred deep convolutional neural network for building a deep model; and then develop an overall deep learning architecture based on the raw pixels of original biomedical images using supervised training. In our model, we do not need the manual design of the feature space, seek an effective feature vector classifier or segment specific detection object and image patches, which are the main technological difficulties in the adoption of traditional image classification methods. Moreover, we do not need to be concerned with whether there are large training sets of annotated biomedical images, affordable parallel computing resources featuring GPUs or long times to wait for training a perfect deep model, which are the main problems to train deep neural networks for biomedical image classification as observed in recent works.

RESULTS

With the utilization of a simple data augmentation method and fast convergence speed, our algorithm can achieve the best accuracy rate and outstanding classification ability for biomedical images. We have evaluated our classifier on several well-known public biomedical datasets and compared it with several state-of-the-art approaches.

CONCLUSIONS

We propose a robust automated end-to-end classifier for biomedical images based on a domain transferred deep convolutional neural network model that shows a highly reliable and accurate performance which has been confirmed on several public biomedical image datasets.

Multichannel Decoded Local Binary Patterns for Content-Based Image Retrieval

Local binary pattern (LBP) is widely adopted for efficient image feature description and simplicity. To describe the color images, it is required to combine the LBPs from each channel of the image. The traditional way of binary combination is to simply concatenate the LBPs from each channel, but it increases the dimensionality of the pattern. In order to cope with this problem, this paper proposes a novel method for image description with multichannel decoded LBPs. We introduce adder- and decoder-based two schemas for the combination of the LBPs from more than one channel. Image retrieval experiments are performed to observe the effectiveness of the proposed approaches and compared with the existing ways of multichannel techniques. The experiments are performed over 12 benchmark natural scene and color texture image databases, such as Corel-1k, MIT-VisTex, USPTex, Colored Brodatz, and so on. It is observed that the introduced multichannel adder- and decoder-based LBPs significantly improve the retrieval performance over each database and outperform the other multichannel-based approaches in terms of the average retrieval precision and average retrieval rate.

Person Re-Identification by Dual-Regularized KISS Metric Learning

Person re-identification aims to match the images of pedestrians across different camera views from different locations. This is a challenging intelligent video surveillance problem that remains an active area of research due to the need for performance improvement. Person re-identification involves two main steps: feature representation and metric learning. Although the keep it simple and straightforward (KISS) metric learning method for discriminative distance metric learning has been shown to be effective for the person re-identification, the estimation of the inverse of a covariance matrix is unstable and indeed may not exist when the training set is small, resulting in poor performance. Here, we present dual-regularized KISS (DR-KISS) metric learning. By regularizing the two covariance matrices, DR-KISS improves on KISS by reducing overestimation of large eigenvalues of the two estimated covariance matrices and, in doing so, guarantees that the covariance matrix is irreversible. Furthermore, we provide theoretical analyses for supporting the motivations. Specifically, we first prove why the regularization is necessary. Then, we prove that the proposed method is robust for generalization. We conduct extensive experiments on three challenging person re-identification datasets, VIPeR, GRID, and CUHK 01, and show that DR-KISS achieves new state-of-the-art performance.

BioSig3D: High Content Screening of Three-Dimensional Cell Culture Models

BioSig3D is a computational platform for high-content screening of three-dimensional (3D) cell culture models that are imaged in full 3D volume. It provides an end-to-end solution for designing high content screening assays, based on colony organization that is derived from segmentation of nuclei in each colony. BioSig3D also enables visualization of raw and processed 3D volumetric data for quality control, and integrates advanced bioinformatics analysis. The system consists of multiple computational and annotation modules that are coupled together with a strong use of controlled vocabularies to reduce ambiguities between different users. It is a web-based system that allows users to: design an experiment by defining experimental variables, upload a large set of volumetric images into the system, analyze and visualize the dataset, and either display computed indices as a heatmap, or phenotypic subtypes for heterogeneity analysis, or download computed indices for statistical analysis or integrative biology. BioSig3D has been used to profile baseline colony formations with two experiments: (i) morphogenesis of a panel of human mammary epithelial cell lines (HMEC), and (ii) heterogeneity in colony formation using an immortalized non-transformed cell line. These experiments reveal intrinsic growth properties of well-characterized cell lines that are routinely used for biological studies. BioSig3D is being released with seed datasets and video-based documentation.