Automated Detection and Classification of Microcalcification Clusters with Enhanced Preprocessing and Fractal Analysis

Abstract

This paper addresses the automated detection of microcalcification clusters from mammogram images by enhanced preprocessing operations on digital mammograms for automated extraction of breast tissue from background, removing artefacts occurring during image registration using X-rays, followed by fractal analysis of suspicious regions. Identification of breast of either left or right and realigning them to a standard position forms a primitive step in preprocessing of mammograms. As the next step in the process, pectoral muscles are separated. Suspicious regions of microcalcifications are identified and are subjected to further analysis of classifying it as benign or malignant. Texture features are representative of its malignancy and fractal analysis was carried out on extracted suspicious regions for its texture features. Principal Component Analysis was carried out to extract optimal features. Ten features were found to be an optimal number of reduced texture features without compromising on classification accuracy. Scaled conjugate Gradient Back propagation network was used for classification using reduced texture features obtained from PCA analysis. By varying hidden layer neurons, accuracy of results achieved by proposed methods is analysed and is calculated to reach maximum accuracy with an optimal level of 15 neurons. Accuracy of 96.3% was achieved with 10 fractal features as input to neural network and 15 hidden layer neurons in neural network designed. The design of architecture is finalised with maximised accuracy for labelling microcalcification clusters as benign or malignant.

Breast Cancer Diagnosis Using Bagging Decision Trees with Improved Feature Selection

“…Histopathology and mammography phenotypes were analyzed by Hamidinekoo et al [12] with the purpose of determining their biological characteristics. The pre-processing approach for mammograms was upgraded by Gowri et al [13] to facilitate the robotic extraction of tissue.…”

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

“…Mahmood et al [16] employed machine learning integrated with the radiomic approach to classifying the textural and statistical features, attaining 98% accuracy and 0.90 AUC. Apart from that, Gowri et al [17] also used textural features with fractal analysis and obtained 96.3% accuracy. Melekoodappattu et al [5] proposed a hybrid extreme machine learning classifier consisting of the extreme learning machine (ELM) with the fruitfly optimization algorithm (ELM-FOA) along with glowworm swarm optimization (GSO).…”

“…Such features included intensity, statistical, shape, and textural features [5]. The grey-level cooccurrence matrix (GLCM), which calculates the occurrence of various grey levels in a region of interest (ROI), is a well-known texture feature and is utilised extensively in the literature [5,[15][16][17]. Nevertheless, all of these features focus on local information of the images and are often burdened with details, resulting in data complexity [18].…”

Multiscale connected chain topological modelling for microcalcification classification

“…Statistical features like surrounding region dependence (SRD), spatial grey-level dependence (SGLD), grey-level run length (GLRL), grey-level difference (GLD) have been used for extracting contrast, entropy, angular second moment (energy), correlation, difference variance, inverse difference moment, skewness, kurtosis, and intensity ratio for the classification of abnormalities [31,32,30,33]. Similarly, multiscale texture features have been extracted using variants of wavelets with various scaling functions [21,34,35,36] and fractal methods [37,38]. Recently, deep learning techniques have been developed for detection and for classifying the lesions in mammograms [39,40,41,42].…”