# Semi-Automatic Segmentation and Ultrasonic Characterization of Solid Breast Lesions

## TL;DR

This paper presents a semi-automatic segmentation method using empirical mode decomposition for breast lesion ultrasound images, combined with spectral and morphometric features for characterization, highlighting effective parameters for differentiating benign and malignant lesions.

## Contribution

Introduces a semi-automatic segmentation approach with empirical mode decomposition and evaluates various sonographic features for breast lesion characterization.

## Key findings

- Echogenicity, heterogeneity, margin definition, aspect ratio, and convexity provided good differentiation results.
- Some features did not yield desired comparative results.
- The method improves robustness in breast lesion classification.

## Abstract

Characterization of breast lesions is an essential prerequisite to detect breast cancer in an early stage. Automatic segmentation makes this categorization method robust by freeing it from subjectivity and human error. Both spectral and morphometric features are successfully used for differentiating between benign and malignant breast lesions. In this thesis, we used empirical mode decomposition method for semi-automatic segmentation. Sonographic features like ehcogenicity, heterogeneity, FNPA, margin definition, Hurst coefficient, compactness, roundness, aspect ratio, convexity, solidity, form factor were calculated to be used as our characterization parameters. All of these parameters did not give desired comparative results. But some of them namely echogenicity, heterogeneity, margin definition, aspect ratio and convexity gave good results and were used for characterization.