Mammogram Classification Using Nonsubsampled Contourlet Transform and Gray-Level Co-Occurrence Matrix

Taifi, Khaddouj; Taifi, Naima; Fakir, Mohamed; Safi, Saïd; Sarfraz, Muhammad

doi:10.4018/978-1-7998-1021-6.ch013

Cited by 5 publications

(3 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It may be noted that the histogram representation of the above two fuzzy Hanman transforms to make them probability-based fuzzy Hanman transforms. The difference between ( 21) and ( 22) is that in (21) p acts as the hesitancy degree and modifies both certainty information value g g and the uncertainty value g g and in (22), ′ p ′ acts as the scale and modifies only g and g . These are higher-level entropy functions accounting for possibilistic, probabilistic, and intuitionistic certainties and uncertainties.…”

Section: The Generalized Adaptive Hanman-anirban Fuzzy Entropy Functi...mentioning

confidence: 99%

“…The computer-aided diagnosis system is proposed in [21] for the categorization of mammograms into fatty, dense, and glandular by removing artifacts, background, and pectoral muscle and applying a decision tree. Non-subsampled contourlet transform and gray level co-occurrence matrix are used in [22] for extracting texture features from the mini-MIAS database. These extracted features are categorized into normal and abnormal mammograms using SVM and KNN classifiers.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Formulation of probability-based pervasive information set features and Hanman transform classifier for the categorization of mammograms

Dabass

Hanmandlu²,

Vig

2021

SN Appl. Sci.

View full text Add to dashboard Cite

With aim of detecting breast cancer at the early stages using mammograms, this study presents the formulation of five feature types by extending the information set to encompass the concept of an intuitionist fuzzy set. The resulting pervasive information set gives not only the certainty of the pixel intensities of mammograms to a class but also the deficiency in the fuzzy modeling referred to as the hesitancy. The generalized adaptive Hanman Anirban fuzzy entropy function is shown to be equivalent to the hesitancy entropy function. The probability-based fuzzy Hanman transform and the pervasive Information with probability taking the role of hesitancy degree help derive the above five feature types termed as probability-based pervasive Information set features. The effectiveness of each feature type is demonstrated on the mini-MIAS and DDSM databases for the multi-class categorization of mammograms using the Hanman transform classifier. The statistical analysis by ANOVA test proves that the features are statistically significant and the experimental results are shown to be clinically relevant by the expert radiologists. The performance of the five feature types is either superior to or equal to that of some deep learning architectures on comparison but they outperform the state-of-the-art literature methods in the classification of breast cancer using mammograms.

show abstract

Section: The Generalized Adaptive Hanman-anirban Fuzzy Entropy Functi...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Formulation of probability-based pervasive information set features and Hanman transform classifier for the categorization of mammograms

Dabass

Hanmandlu²,

Vig

2021

SN Appl. Sci.

View full text Add to dashboard Cite

show abstract

“…Researchers have also focused on the fusion of existing texture extraction methods and select effective features. These include the integration of MRF and entropy features [19], the combination of GLCM and wavelet analysis (WA) [20], and the fusion of LBP variants [21]. Researchers are also still investigating new algorithms to extract multi-scale and rotation invariant texture features.…”

Section: Introductionmentioning

confidence: 99%

Grading of Metacarpophalangeal Rheumatoid Arthritis on Ultrasound Images Using Machine Learning Algorithms

et al. 2020

View full text Add to dashboard Cite

The grading evaluation of metacarpophalangeal rheumatoid arthritis (RA) ultrasonic images is a diagnostic challenge that heavily relies on the expertise of trained sonographers. This study presents a grading method for detecting and estimating the geometric and texture features of synovium thickening and bone erosion. Unlike previous studies in this area, this work uses the metrics and texture features of region of interest (ROI). The highlighted feature of metacarpophalangeal bone and the dark feature of the synovial thickening are extracted simultaneously by the segmented method based on the Gaussian scale space. The segmented results are analyzed to extract three quantitative geometric parameters, which are combined with gray-level co-occurrence matrix (GLCM) statistic texture features to describe the ultrasonic image of metacarpophalangeal RA. To obtain the preferable ability of classification, we applied a support vector machine (SVM) and various feature descriptors, including GLCM, local binary patterns (LBP), and GLCM + LBP, to grade the ultrasonic image of metacarpophalangeal RA. Results show that the SVM, based on our feature descriptor, provides the highest accuracy of up to 92.50%, of the four descriptors. The SVM based on GLCM+LBP descriptor shows better accuracy (86.55%) than either SVM + LBP (85.43%) or SVM+GLCM (82.51%) for discriminating among four grade RA ultrasonic images. Overall, this methodology points to a significant grading of metacarpophalangeal RA ultrasound images without medical expert analysis or blood sample analysis, such as detecting C-reactive protein, measuring erythrocyte sedimentation rate, and testing rheumatoid factor. INDEX TERMS Computer aided diagnosis, metacarpophalangeal rheumatoid arthritis, machine learning, Ultrasonic imaging.

show abstract

Breast Mass Classification Using Convolutional Neural Network

Nemade¹,

Pathak²,

Dubey³

et al. 2022

Lecture Notes in Networks and Systems

View full text Add to dashboard Cite

Mammogram Classification Using Nonsubsampled Contourlet Transform and Gray-Level Co-Occurrence Matrix

Cited by 5 publications

References 10 publications

Formulation of probability-based pervasive information set features and Hanman transform classifier for the categorization of mammograms

Formulation of probability-based pervasive information set features and Hanman transform classifier for the categorization of mammograms

Grading of Metacarpophalangeal Rheumatoid Arthritis on Ultrasound Images Using Machine Learning Algorithms

Breast Mass Classification Using Convolutional Neural Network

Contact Info

Product

Resources

About