Microcalcification detection with and without CAD system (LIBCAD): A comparative study

Razek, Naglaa M. Abdel; Yousef, Waleed A.; Mustafa, Waleed

doi:10.1016/j.ejrnm.2013.01.009

Cited by 4 publications

(1 citation statement)

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“…Determining both lesions will help to provide the most appropriate treatment method and thus, this increases the survival rate [6]. However, digitised mammograms with low image quality will reduce lesion detectability and differentiation [7]. As a result, many patients will be asked to undergo further investigations such as an invasive biopsy.…”

Section: Introductionmentioning

confidence: 99%

Automated Classification of Breast Cancer Lesions for Digitised Mammograms via Computer-Aided Diagnosis System

Suradi

Abdullah

Isa

2021

J. Appl. Sci. Process Eng.

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Women with breast cancer have a high risk of death. Digitised mammograms can be used to detect the early stage of breast cancer. However, digitised mammograms suffer low contrast appearances that may lead to misdiagnosis. This paper proposes a Computer-Aided Diagnosis (CAD) system of automated classification of breast cancer lesions using a modified image processing technique of Fuzzy Anisotropic Diffusion Histogram Equalization Contrast Adaptive Limited (FADHECAL) incorporated with Multilevel Otsu Thresholding on digitised mammograms. Four main blocks were used in this CAD system, namely; (i) Pre-processing and Enhancement block; (ii) Segmentation block; (iii) Region of Interests (ROIs) Extraction block; and (iv) Classification block. The CAD system was tested on 30 digitised mammograms retrieved from the Mini-Mammographic Image Analysis Society (MIAS) database with various degrees of severity and background tissues. The proposed CAD system showed a high accuracy of 96.67% for the detection of breast cancer lesions.

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Section: Introductionmentioning

confidence: 99%