# Unsupervised Method to Localize Masses in Mammograms

## TL;DR

This paper introduces an automated hierarchical clustering-based method for detecting masses in mammograms, improving early breast cancer diagnosis by reducing false positives and demonstrating effectiveness on public datasets.

## Contribution

It presents a novel unsupervised approach combining hierarchical clustering and feature extraction for mass localization in mammograms, addressing challenges of tissue similarity.

## Key findings

- Effective mass detection demonstrated on mini-MIAS and DDSM datasets.
- Improved ROC performance compared to existing methods.
- Automated segmentation reduces false positives in mammogram analysis.

## Abstract

Breast cancer is one of the most common and prevalent type of cancer that mainly affects the women population. chances of effective treatment increases with early diagnosis. Mammography is considered one of the effective and proven techniques for early diagnosis of breast cancer. Tissues around masses look identical in mammogram, which makes automatic detection process a very challenging task. They are indistinguishable from the surrounding parenchyma. In this paper, we present an efficient and automated approach to segment masses in mammograms. The proposed method uses hierarchical clustering to isolate the salient area, and then features are extracted to reject false detection. We applied our method on two popular publicly available datasets (mini-MIAS and DDSM). A total of 56 images from mini-mias database, and 76 images from DDSM were randomly selected. Results are explained in-terms of ROC (Receiver Operating Characteristics) curves and compared with the other techniques. Experimental results demonstrate the efficiency and advantages of the proposed system in automatic mass identification in mammograms.