Title: Mass detection in mammographic images using improved marker-controlled watershed approach

Authors: Pratap S. Vikhe; Vaishali V. Mandhare; Chandrakant B. Kadu

Addresses: Department of Instrumentation and Control Engineering, Pravara Rural Engineering College, Loni, 413736, India; Savitribai Phule Pune University, Ganeshkhind, Pune, Maharashtra, India ' Department of Computer Engineering, Pravara Rural Engineering College, Loni, 413736, India; Savitribai Phule Pune University, Ganeshkhind, Pune, Maharashtra, India ' Department of Instrumentation and Control Engineering, Pravara Rural Engineering College, Loni, 413736, India; Savitribai Phule Pune University, Ganeshkhind, Pune, Maharashtra, India

Abstract: Mass detection in mammogram plays a vital role for early diagnosis of breast cancer. However, screening of masses is a challenging task for radiologist, due to contrast variation, noisy mammographic images and imprecise edges. In this paper, improved marker-controlled watershed approach is presented to segment and detects precise suspicious regions from mammograms. Morphological operations and threshold technique has been used in the proposed algorithm, to suppress artefacts and pectoral region. Magnitude gradient was computed to obtain mass edges. Finally, internal and external markers were determined and watershed transform was applied on modified gradient image, to segregate suspicious region. Proposed approach was applied on 140 mammograms from two datasets, MIAS and DDSM. The performance of proposed approach in terms of true positive fraction yields 93.7% and 94.3% respectively, at the rate of 0.72 and 0.45 average false positive per image. Thus, achieved results depicts that the proposed approach gives better results for mass detection, helping radiologists in diagnosis at an early stage.

Keywords: watershed transform; mass detection; marker-controlled; segmentation; mammograms.

DOI: 10.1504/IJBET.2022.125103

International Journal of Biomedical Engineering and Technology, 2022 Vol.40 No.1, pp.70 - 98

Received: 31 Aug 2019
Accepted: 14 Jan 2020
Published online: 30 Aug 2022 *

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