A multiresolution diffused expectation-maximization algorithm for medical image segmentation

Multiplex Immunofluorescence and the Digital Image Analysis Workflow for Evaluation of the Tumor Immune Environment in Translational Research

A robust understanding of the tumor immune environment has important implications for cancer diagnosis, prognosis, research, and immunotherapy. Traditionally, immunohistochemistry (IHC) has been regarded as the standard method for detecting proteins in situ, but this technique allows for the evaluation of only one cell marker per tissue sample at a time. However, multiplexed imaging technologies enable the multiparametric analysis of a tissue section at the same time. Also, through the curation of specific antibody panels, these technologies enable researchers to study the cell subpopulations within a single immunological cell group. Thus, multiplexed imaging gives investigators the opportunity to better understand tumor cells, immune cells, and the interactions between them. In the multiplexed imaging technology workflow, once the protocol for a tumor immune micro environment study has been defined, histological slides are digitized to produce high-resolution images in which regions of interest are selected for the interrogation of simultaneously expressed immunomarkers (including those co-expressed by the same cell) by using an image analysis software and algorithm. Most currently available image analysis software packages use similar machine learning approaches in which tissue segmentation first defines the different components that make up the regions of interest and cell segmentation, then defines the different parameters, such as the nucleus and cytoplasm, that the software must utilize to segment single cells. Image analysis tools have driven dramatic evolution in the field of digital pathology over the past several decades and provided the data necessary for translational research and the discovery of new therapeutic targets. The next step in the growth of digital pathology is optimization and standardization of the different tasks in cancer research, including image analysis algorithm creation, to increase the amount of data generated and their accuracy in a short time as described herein. The aim of this review is to describe this process, including an image analysis algorithm creation for multiplex immunofluorescence analysis, as an essential part of the optimization and standardization of the different processes in cancer research, to increase the amount of data generated and their accuracy in a short time.

An Intelligent Model for the Detection of White Blood Cells using Artificial Intelligence

BACKGROUND AND OBJECTIVE

The automatic detection and counting of white blood cells (WBCs) play a vital role in the diagnosis of hematological diseases. Computer-aided methods are prevalent in the detection of WBCs because the manual process involves several complexities. In this article, a complete automatic detection algorithm to recognize the WBCs embedded in cluttered and complicated smear images of blood is designed.

METHODS

The proposed algorithm uses the ellipse detection approach to approximate the presence of WBCs in the Blood. A newly designed artificial electric field algorithm with novel velocity and position bound (AEFA-C) is employed for this purpose. The problem of detection of WBCs is transformed into an optimization problem where the random candidate solutions (ellipses) are efficiently mapped. These candidate ellipses are mapped onto the edge map of the smear image, and a complete mapping is obtained using the AEFA-C algorithm.

RESULTS

The effectiveness of the AEFA-C based detector is tested over the 60 smear images of the blood, having all the five types of WBCs or leukocytes. The developed algorithm obtained an overall detection accuracy of 96.90%. Further, the robustness test is performed on the same dataset which justifies that the technique can handle the different noises with the detection accuracy of 90.33%. Also, the comparative study of the proposed detection algorithm with the state-of-art detection algorithms is carried out.

CONCLUSIONS

The experimental results demonstrate the efficiency of the proposed scheme for the detection of the WBCs in terms of detection accuracy, stability, and robustness and its outperformance over the state-of-art algorithms.

Controlled differential evolution based detection of neovascularization on optic disc using support vector machine

The severe stage of Diabetic Retinopathy (DR) is characterized by the growth of new blood vessels which is called Neovascularization (NV). The abnormally grown blood vessels on the disc are breakable in nature thus the patient is at high risk of sudden blindness. Therefore, the significance of early and accurate detection of Neovascularization on Disc (NVD) should not be neglected. This paper presents an automatic detection of the optic disc using a Controlled Differential Evolution (CDE) algorithm. Further, the Region of Interest (ROI) is created automatically by extending the extreme boundaries of the optic disc by 100 pixels to ensure the presence of NV around the optic disc also. From the ROI so created, blood vessels are segmented using multi-scale Gabor filtering and subsequently, both the morphological and textural features are extracted. Simultaneously, statistical features are directly extracted from the earlier created ROI. Finally, the fundus image is classified by a Support Vector Machine (SVM) using the extracted features from all three feature sets. From each individual image, 16 features are extracted and the feature dimension is reduced to 13 using a sequential backward feature (SBF) selection algorithm. The optimal features are obtained from a total of 205 fundus images, which consists of 99 NVD positive and 106 NVD negative images. This paper attains an average accuracy of 98.75%, the specificity of 100%, the sensitivity of 97.8%, and area under the curve (AUC) as 100% when tested over image selected randomly.

An improved computer vision method for white blood cells detection

The automatic detection of white blood cells (WBCs) still remains as an unsolved issue in medical imaging. The analysis of WBC images has engaged researchers from fields of medicine and computer vision alike. Since WBC can be approximated by an ellipsoid form, an ellipse detector algorithm may be successfully applied in order to recognize such elements. This paper presents an algorithm for the automatic detection of WBC embedded in complicated and cluttered smear images that considers the complete process as a multiellipse detection problem. The approach, which is based on the differential evolution (DE) algorithm, transforms the detection task into an optimization problem whose individuals represent candidate ellipses. An objective function evaluates if such candidate ellipses are actually present in the edge map of the smear image. Guided by the values of such function, the set of encoded candidate ellipses (individuals) are evolved using the DE algorithm so that they can fit into the WBCs which are enclosed within the edge map of the smear image. Experimental results from white blood cell images with a varying range of complexity are included to validate the efficiency of the proposed technique in terms of its accuracy and robustness.

White blood cell segmentation by circle detection using electromagnetism-like optimization

Medical imaging is a relevant field of application of image processing algorithms. In particular, the analysis of white blood cell (WBC) images has engaged researchers from fields of medicine and computer vision alike. Since WBCs can be approximated by a quasicircular form, a circular detector algorithm may be successfully applied. This paper presents an algorithm for the automatic detection of white blood cells embedded into complicated and cluttered smear images that considers the complete process as a circle detection problem. The approach is based on a nature-inspired technique called the electromagnetism-like optimization (EMO) algorithm which is a heuristic method that follows electromagnetism principles for solving complex optimization problems. The proposed approach uses an objective function which measures the resemblance of a candidate circle to an actual WBC. Guided by the values of such objective function, the set of encoded candidate circles are evolved by using EMO, so that they can fit into the actual blood cells contained in the edge map of the image. Experimental results from blood cell images with a varying range of complexity are included to validate the efficiency of the proposed technique regarding detection, robustness, and stability.

Multi-field-of-view framework for distinguishing tumor grade in ER+ breast cancer from entire histopathology slides

Modified Bloom-Richardson (mBR) grading is known to have prognostic value in breast cancer (BCa), yet its use in clinical practice has been limited by intra- and interobserver variability. The development of a computerized system to distinguish mBR grade from entire estrogen receptor-positive (ER+) BCa histopathology slides will help clinicians identify grading discrepancies and improve overall confidence in the diagnostic result. In this paper, we isolate salient image features characterizing tumor morphology and texture to differentiate entire hematoxylin and eosin (H and E) stained histopathology slides based on mBR grade. The features are used in conjunction with a novel multi-field-of-view (multi-FOV) classifier--a whole-slide classifier that extracts features from a multitude of FOVs of varying sizes--to identify important image features at different FOV sizes. Image features utilized include those related to the spatial arrangement of cancer nuclei (i.e., nuclear architecture) and the textural patterns within nuclei (i.e., nuclear texture). Using slides from 126 ER+ patients (46 low, 60 intermediate, and 20 high mBR grade), our grading system was able to distinguish low versus high, low versus intermediate, and intermediate versus high grade patients with area under curve values of 0.93, 0.72, and 0.74, respectively. Our results suggest that the multi-FOV classifier is able to 1) successfully discriminate low, medium, and high mBR grade and 2) identify specific image features at different FOV sizes that are important for distinguishing mBR grade in H and E stained ER+ BCa histology slides.