Analysis of microcalcification features for pathological classification of mammograms

Roty, Seng; Wiratkapun, Cholatip; Tanawongsuwan, Rawesak; Phongsuphap, Sukanya

doi:10.1109/bmeicon.2017.8229131

Cited by 7 publications

(3 citation statements)

References 10 publications

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Section: Introductionmentioning

confidence: 99%

“…This is true especially when the microcalcifications appear in low contrast and are obscured by the background tissue of the images [10]. Here, human errors based on subjective evaluations may lead to unnecessary biopsy procedures, which can cause harm and anxiety for patients [11]. In order to improve the accuracy of assessing microcalcifications, numerous studies have been conducted to develop computational approaches that could potentially aid radiologists in distinguishing benign and malignant microcalcifications [3].…”

Section: Introductionmentioning

confidence: 99%

“…Another important phase that influences classification performance is feature extraction [11]. Feature extraction methodologies analyse images to extract the most prevalent features and are employed as inputs to machine learning classifiers to distinguish between benign and malignant classes.…”

Section: Introductionmentioning

confidence: 99%

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Persistent Homology-Based Machine Learning Method for Filtering and Classifying Mammographic Microcalcification Images in Early Cancer Detection

Malek

Alias

Razak

et al. 2023

Cancers

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Microcalcifications in mammogram images are primary indicators for detecting the early stages of breast cancer. However, dense tissues and noise in the images make it challenging to classify the microcalcifications. Currently, preprocessing procedures such as noise removal techniques are applied directly on the images, which may produce a blurry effect and loss of image details. Further, most of the features used in classification models focus on local information of the images and are often burdened with details, resulting in data complexity. This research proposed a filtering and feature extraction technique using persistent homology (PH), a powerful mathematical tool used to study the structure of complex datasets and patterns. The filtering process is not performed directly on the image matrix but through the diagrams arising from PH. These diagrams will enable us to distinguish prominent characteristics of the image from noise. The filtered diagrams are then vectorised using PH features. Supervised machine learning models are trained on the MIAS and DDSM datasets to evaluate the extracted features’ efficacy in discriminating between benign and malignant classes and to obtain the optimal filtering level. This study reveals that appropriate PH filtering levels and features can improve classification accuracy in early cancer detection.

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Section: Introductionmentioning

confidence: 99%