A Wavelet-packet-based approach for breast cancer classification

Torabi, Meysam; Razavian, Seiied-Mohammad-Javad; Vaziri, Reza; Vosoughi-Vahdat, Bijan

doi:10.1109/iembs.2011.6091263

Cited by 5 publications

(3 citation statements)

References 15 publications

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“…The study applied the phase portrait method to the detection of architectural distortion in mammograms. Torabi, et al, [129] applied Wavelet packet analysis on the two-dimensional histogram matrices of mammography to generate the filter banks to extract statistical features -skewness and kurtosis. Using the 5-fold cross-validation protocol, the authors claimed that their method improved the detection accuracy of architectural distortion.…”

Section: ) Architectural Distortionmentioning

confidence: 99%

A State-of-the-Art Survey on Deep Learning Methods for Detection of Architectural Distortion From Digital Mammography

Oyelade

Ezugwu

2020

IEEE Access

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Breast cancer is a type of cancer that has risen to be the second cause of death among women. Classification of breast tissues into normal, benign, or malignant depends on the presence of abnormalities like microcalcifications, masses, architectural distortions, and asymmetries. Architectural distortion (AD) is subtle in detection with no association with masses but shows the abnormal arrangement of tissue strands, often in a radial, spiculation, or random pattern. It is widely rated as the third symptom of breast cancer which is the most commonly missed abnormality. Most computational approaches characterizing abnormalities in breast images often concentrate on the detection of microcalcification and masses with architectural distortions appearing as a secondary finding. The subtle nature and a minimal occurrence of architectural distortions may seem to complicate computational approaches for its detection. As a result, little research interest has been recorded in this area. It is widely reported that some cases of recent breast cancer are wrongly diagnosed due to the omission in detecting the presence of architectural distortion at the early stage of the disease. However, we discovered that most computational solutions to early detection of breast cancer are focused mainly on detecting other abnormalities such as masses and microcalcification, which are some evidence of the advanced stage of the disease. To emphasise the little efforts channeled towards detection of AD compared to other abnormalities, this article aims to detail the review of such studies in the last decade. To the best of our knowledge, this study presents the first review which focuses on the detection of architectural distortion (AD) from mammographic images. Furthermore, this article presents a comprehensive review of approaches, advances, and challenges on the computational methods for detecting AD, with the sole aim of advancing the use of deep learning models in detecting AD. Moreover, a comparative study of performance analyses of articles surveyed in this article is investigated. Our findings revealed that about 70% of the existing literature adopted Gabor Filters, while just less than 10% leveraged on the state-ofthe-art performances recorded in computer vision and deep learning, in building outstanding computational models for the detection of AD. The current study also discovered that using a deep learning approach, such as the convolution neural network (CNN) method, can yield a significant increase in performance for the task of detection of architectural distortions. This assertion is based on literature results obtained using the CNN, which generates an accuracy of 99.4% compared to the use of Gabor filters method, which accounts for 95% accuracy.

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Section: ) Architectural Distortionmentioning

confidence: 99%

A State-of-the-Art Survey on Deep Learning Methods for Detection of Architectural Distortion From Digital Mammography

Oyelade

Ezugwu

2020

IEEE Access

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“…As there are many similarities between mitotic cells and nonmitotic ones, the extracted features must be discriminant. The features that we extract are as follows: 14 haralick features derived from grey level cooccurrence matrices (GLCMs) [22], 11 features obtained from run-length matrices (CLRLMS) [23], 17 features achieved from complete local binary pattern (CLBP) with radius = 2 and the number of neighboring pixels = 16 [24], 4 statistical features comprising grey level mean, variance, and third and fourth moments, 32 mean and energy features obtained from two level decomposition of packet wavelet [25], and 32 energy features obtained from Gabor filtering based on 8 directions and 4 frequencies [26]. Since the histopathology images are RGB, the features are extracted from the three colour components; therefore the final length of feature vectors is 330.…”

Section: Feature Extractionmentioning

confidence: 99%

Intelligent CAD System for Automatic Detection of Mitotic Cells from Breast Cancer Histology Slide Images Based on Teaching-Learning-Based Optimization

Nateghi

Danyali

Helfroush

et al. 2014

Computational Biology Journal

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This paper introduces a computer-assisted diagnosis (CAD) system for automatic mitosis detection from breast cancer histopathology slide images. In this system, a new approach for reducing the number of false positives is proposed based on Teaching-Learning-Based optimization (TLBO). The proposed CAD system is implemented on the histopathology slide images acquired by Aperio XT scanner (scanner A). In TLBO algorithm, the number of false positives (falsely detected nonmitosis candidates as mitosis ones) is defined as a cost function and, by minimizing it, many of nonmitosis candidates will be removed. Then some color and texture (textural) features such as those derived from cooccurrence and run-length matrices are extracted from the remaining candidates and finally mitotic cells are classified using a specific support vector machine (SVM) classifier. The simulation results have proven the claims about the high performance and efficiency of the proposed CAD system.

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“…For example, in addition to two projects for diagnosing AD by tissue analysis [2], [3], we also utilized this method for diagnosis of Multiple Sclerosis (MS) [4]. Additionally, we could diagnose various breast diseases with the help of texture analysis [5]- [7].…”

Section: Introductionmentioning

confidence: 99%

Parallel nonlinear analysis of weighted brain's gray and white matter images for Alzheimer's dementia diagnosis

Razavian

Torabi

Kim

2010

2010 Annual International Conference of the IEEE Engineering in Medicine and Biology

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In this study, we are proposing a novel nonlinear classification approach to discriminate between Alzheimer's Disease (AD) and a control group using T1-weighted and T2-weighted Magnetic Resonance Images (MRI's) of brain. Since T1-weighted images and T2-weighted images have inherent physical differences, obviously each of them has its own particular medical data and hence, we extracted some specific features from each. Then the variations of the relevant eigenvalues of the extracted features were tracked to pick up the most informative ones. The final features were assigned to two parallel systems to be nonlinearly categorized. Considering the fact that AD defects the white and gray regions of brain more than its black and marginal regions, and also since T1-weighted has more medical data of white and gray regions than T2-weighted images, we put optimal weights for the two outputs. Combination of these two results made the final decision of AD diagnosis system. The dataset includes 60 T1-weighted images and 60 T2-weighted images of normal and abnormal cases. The dataset which includes different cross-sections of the brain, after an accurate registration, was split to two groups of test set (40 percent of the dataset) and training set (60 percent of the dataset). The results demonstrate more than two thirds of accuracy in detection of normal and abnormal images.

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A Wavelet-packet-based approach for breast cancer classification

Cited by 5 publications

References 15 publications

A State-of-the-Art Survey on Deep Learning Methods for Detection of Architectural Distortion From Digital Mammography

A State-of-the-Art Survey on Deep Learning Methods for Detection of Architectural Distortion From Digital Mammography

Intelligent CAD System for Automatic Detection of Mitotic Cells from Breast Cancer Histology Slide Images Based on Teaching-Learning-Based Optimization

Parallel nonlinear analysis of weighted brain's gray and white matter images for Alzheimer's dementia diagnosis

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