Analysis and Classification of Breast Abnormalities Using Ultrasound Images

Abstract

The present research work has been carried out with an aim to enhance the diagnostic potential of B-mode ultrasound imaging modality for the diagnosis of breast abnormalities. To achieve this objective exhaustive experiments have been carried out in the present research work to (a) analyse the effect of despeckle filtering algorithms on breast ultrasound images, (b) analyse the effect of despeckle filtering algorithms on segmentation of breast tumors, (c) analyse the effect of despeckle filtering algorithms on classification of breast tumors, (d) design an efficient local binary pattern (LBP) based CAD system for classification of breast tumors, (e) design an efficient convolutional neural network based CAD system for classification of breast tumors. For carrying out the experiments a comprehensive dataset of 100 B-mode breast ultrasound images comprising of cysts, fibroadenomas, lipomas in benign category, ductal and lobular carcinomas in malignant category has been taken from a standard benchmark database, ultrasoundcases.info. Initially exhaustive experimentations have been carried out to analyze the effect of 42 despeckle filtering algorithms taken from various filter categories namely (a) Local statistics based filters, (b) Fourier filters, (c) Fuzzy filters, (d) Multiscale filters, (e) Non-local mean filters, (f) Non-linear iterative filters, (g) Total variation filters and (h) Hybrid filters. The resultant despeckled images have been used for objective assessment and subjective assessment. For the objective assessment, an image quality metric named structure and edge preservation index (SEPI) has been proposed. This index quantifies the edge preservation and structure preservation capability of the filtering algorithm.