Brain tumor classification and segmentation using sparse coding and dictionary learning

Brain Tumor MR Image Classification Using Convolutional Dictionary Learning With Local Constraint

Brain tumor image classification is an important part of medical image processing. It assists doctors to make accurate diagnosis and treatment plans. Magnetic resonance (MR) imaging is one of the main imaging tools to study brain tissue. In this article, we propose a brain tumor MR image classification method using convolutional dictionary learning with local constraint (CDLLC). Our method integrates the multi-layer dictionary learning into a convolutional neural network (CNN) structure to explore the discriminative information. Encoding a vector on a dictionary can be considered as multiple projections into new spaces, and the obtained coding vector is sparse. Meanwhile, in order to preserve the geometric structure of data and utilize the supervised information, we construct the local constraint of atoms through a supervised k-nearest neighbor graph, so that the discrimination of the obtained dictionary is strong. To solve the proposed problem, an efficient iterative optimization scheme is designed. In the experiment, two clinically relevant multi-class classification tasks on the Cheng and REMBRANDT datasets are designed. The evaluation results demonstrate that our method is effective for brain tumor MR image classification, and it could outperform other comparisons.

Sparse Coding for Brain Tumor Segmentation Based on the Non-Linear Features

The main aim of brain Magnetic Resonance Image (MRI) segmentation is to extractthe significant objects like tumors for better diagnosis and proper treatment. As the brain tumors are distinct in the sense of shapes, location, and intensity it is hard to define a general algorithm for the tumor segmentation. Accurate extraction of tumors from the brain MRIs is a challenging task due to the complex anatomical structure of brain tissues in addition to the existence of intensity inhomogeneity, partial volume effects, and noise. In this paper, a method of Sparse coding based on non-linear features is proposed for the tumor segmentation from MR images of the brain. Initially, first and second-order statistical eigenvectors of 3 × 3 size are extracted from the MRIs then the process of Sparse coding is applied to them. The kernel dictionary learning algorithm is employed to obtain the non-linear features from these processed vectors to build two individual adaptive dictionaries for healthy and pathological tissues. This work proposes dictionary learning based kernel clustering algorithm to code the pixels, and then the target pixelsare classified by utilizing the method of linear discrimination. The proposed technique is applied to several tumor MRIs, taken from the BRATS database. This technique overcomes the problem of linear inseparability produced by the high level intensity similarity between the normal and abnormal tissues of the given brain image. All the performance parameters are high for the proposed technique. Comparison of the results with some other existing methods such as Fuzzy – C- Means (FCM), Hybrid k-Mean Graph Cut (HKMGC) and Neutrosophic Set – Expert Maximum Fuzzy Sure Entropy (NS-EMFSE) demonstrates that the proposed sparse coding method is effective in segmenting the brain tumor regions.

Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review

OBJECTIVES

Medical image analysis practices face challenges that can potentially be addressed with algorithm-based segmentation tools. In this study, we map the field of automatic MR brain lesion segmentation to understand the clinical applicability of prevalent methods and study designs, as well as challenges and limitations in the field.

DESIGN

Scoping review.

SETTING

Three databases (PubMed, IEEE Xplore and Scopus) were searched with tailored queries. Studies were included based on predefined criteria. Emerging themes during consecutive title, abstract, methods and whole-text screening were identified. The full-text analysis focused on materials, preprocessing, performance evaluation and comparison.

RESULTS

Out of 2990 unique articles identified through the search, 441 articles met the eligibility criteria, with an estimated growth rate of 10% per year. We present a general overview and trends in the field with regard to publication sources, segmentation principles used and types of lesions. Algorithms are predominantly evaluated by measuring the agreement of segmentation results with a trusted reference. Few articles describe measures of clinical validity.

CONCLUSIONS

The observed reporting practices leave room for improvement with a view to studying replication, method comparison and clinical applicability. To promote this improvement, we propose a list of recommendations for future studies in the field.

FDSR: A new fuzzy discriminative sparse representation method for medical image classification

Recent developments in medical image analysis techniques make them essential tools in medical diagnosis. Medical imaging is always involved with different kinds of uncertainties. Managing these uncertainties has motivated extensive research on medical image classification methods, particularly for the past decade. Despite being a powerful classification tool, the sparse representation suffers from the lack of sufficient discrimination and robustness, which are required to manage the uncertainty and noisiness in medical image classification issues. It is tried to overcome this deficiency by introducing a new fuzzy discriminative robust sparse representation classifier, which benefits from the fuzzy terms in its optimization function of the dictionary learning process. In this work, we present a new medical image classification approach, fuzzy discriminative sparse representation (FDSR). The proposed fuzzy terms increase the inter-class representation difference and the intra-class representation similarity. Also, an adaptive fuzzy dictionary learning approach is used to learn dictionary atoms. FDSR is applied on Magnetic Resonance Images (MRI) from three medical image databases. The comprehensive experimental results clearly show that our approach outperforms its series of rival techniques in terms of accuracy, sensitivity, specificity, and convergence speed.

Liver segmentation from CT images using a sparse priori statistical shape model (SP-SSM)

This study proposes a new liver segmentation method based on a sparse a priori statistical shape model (SP-SSM). First, mark points are selected in the liver a priori model and the original image. Then, the a priori shape and its mark points are used to obtain a dictionary for the liver boundary information. Second, the sparse coefficient is calculated based on the correspondence between mark points in the original image and those in the a priori model, and then the sparse statistical model is established by combining the sparse coefficients and the dictionary. Finally, the intensity energy and boundary energy models are built based on the intensity information and the specific boundary information of the original image. Then, the sparse matching constraint model is established based on the sparse coding theory. These models jointly drive the iterative deformation of the sparse statistical model to approximate and accurately extract the liver boundaries. This method can solve the problems of deformation model initialization and a priori method accuracy using the sparse dictionary. The SP-SSM can achieve a mean overlap error of 4.8% and a mean volume difference of 1.8%, whereas the average symmetric surface distance and the root mean square symmetric surface distance can reach 0.8 mm and 1.4 mm, respectively.

Alzheimer's disease detection via automatic 3D caudate nucleus segmentation using coupled dictionary learning with level set formulation

BACKGROUND AND OBJECTIVE

This paper presents a novel method for Alzheimer's disease classification via an automatic 3D caudate nucleus segmentation.

METHODS

The proposed method consists of segmentation and classification steps. In the segmentation step, we propose a novel level set cost function. The proposed cost function is constrained by a sparse representation of local image features using a dictionary learning method. We present coupled dictionaries: a feature dictionary of a grayscale brain image and a label dictionary of a caudate nucleus label image. Using online dictionary learning, the coupled dictionaries are learned from the training data. The learned coupled dictionaries are embedded into a level set function. In the classification step, a region-based feature dictionary is built. The region-based feature dictionary is learned from shape features of the caudate nucleus in the training data. The classification is based on the measure of the similarity between the sparse representation of region-based shape features of the segmented caudate in the test image and the region-based feature dictionary.

RESULTS

The experimental results demonstrate the superiority of our method over the state-of-the-art methods by achieving a high segmentation (91.5%) and classification (92.5%) accuracy.

CONCLUSIONS

In this paper, we find that the study of the caudate nucleus atrophy gives an advantage over the study of whole brain structure atrophy to detect Alzheimer's disease.