A novel framework for segmentation of uterus fibroids in ultrasound images using machine learning models

Abstract

A tumour of non-cancerous structure that appears in uterus during child-bearing years is uterine fibroids. Thus, it is necessary to design a fibroid detection system for the fibroid ablation. Various methods developed for the detection of fibroids are easily affected by the image artefacts as they do not take into consideration the spatial information and have lower efficiency problems for fibroid segmentation. This paper puts forward a method for segmentation for fibroid detection. The proposed segmentation model overcomes the drawbacks of existing methodologies of fibroid detection in all stages. Here, the speckle noise existing in the noisy input image can be removed by using IGDT-DWT method and EMD-GCLAHE method. After contrast enhancement, the segmentation of the contrast-enhanced image is done using a novel clustering algorithm namely PC-K-mean algorithm. The proposed segmentation algorithm effectively detects the fibroids, which is experimentally proved by comparing it with existing classifiers.