Artificial life for segmentation of fusion ultrasound images of breast abnormalities. (November 2022)

• Record Type: Journal Article
• Authors: Karunanayake, Nalan; Lohitvisate, Wanrudee; Makhanov, Stanislav S.
• Is Part Of: Pattern recognition. Volume 131(2022)
• Publication Date: 2022-11
• Subjects: Artificial life -- Fusion image -- Medical image segmentation -- Genetic algorithm -- Ultrasound images -- Breast cancer; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2022.108838
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: A new method of segmentation medical images which combines AI, fusion and artificial life. A new variant of fusion of the US and the elastography images. Artificial agents with the ability to find strong edges and close the boundary gaps. Testing against 85 preceding segmentation methods. Video demo at http://shorturl.at/htBW9 . Abstract: Segmentation of cancerous tumors in ultrasound (US) images of human organs is one of the critical problems in medical imaging. The US images are characterized by low contrast, irregular shapes, high levels of speckle-noise and acoustic shadows, making it difficult to segment the tumor. Yet, US imaging is considered one of the most inexpensive and safe imaging tests available to detect cancer in its early stages. However, an automatic segmentation method applicable to all types of US imagery does not exist. This paper proposes a novel segmentation method that combines image fusion, artificial life (AL) and a genetic algorithm (GA). The new algorithm has been applied to US images of breast cancer. The method is based on tracing agents (TA), which are artificial organisms with memory and the ability to communicate. They live inside a fusion image generated from the US and the elastography (EL) images. The TA can recognize the patterns of strong edges and boundary gaps allowing to outline the tumor. The new model has been tested against six types of segmentation models, i.e., machine learning, active contours, level set models, superpixel models, edge linking models and selected hybrid methods. The experiments include 16 state-of-the-art methods, which outperform 69 recent and classical segmentation routines. The tests were run on 395 breast cancer images from http://onlinemedicalimages.com and https://www.ultrasoundcases.info/ . TA training employs a GA. The model has been verified on "hard" cases (complex shapes, boundary leakage, and noisy edge maps). The proposed algorithm produces more accurate results than the reference methods on high complexity images. A video demo of the algorithm is at http://shorturl.at/htBW9 .