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An adaptive semi-supervised Fuzzy growcut algorithm to segment masses of regions of interest of mammographic images

APPLIED SOFT COMPUTING 2016年 46卷 613-628页

作者： Cordeiro, Filipe R. Santos, Wellington P. Silva-Filho, Abel G. Univ Fed Pernambuco Informat Ctr Recife PE Brazil Univ Fed Pernambuco Dept Biomed Engn Recife PE Brazil

According to the World Health Organization, breast cancer is the most common cancer in women worldwide, becoming one of the most fatal types of cancer. Mammography image analysis is still the most effective imaging technology for breast cancer diagnosis, which is based on texture and shape analysis of mammary lesions. The growcut algorithm is a general-purpose segmentation method based on cellular automata, able to perform relatively accurate segmentation through the adequate selection of internal and external seed points. In this work we propose an adaptive semi-supervised version of the Grow Cut algorithm, based on the modification of the automaton evolution rule by adding a Gaussian fuzzy membership function in order to model non-defined borders. In our proposal, manual selection of seed points of the suspicious lesion is changed by a semiautomatic stage, where just the internal points are selected by using a differential evolution algorithm. We evaluated our proposal using 57 lesion images obtained from MiniMIAS database. Results were compared with the semi-supervised state-of-the-art approaches BEMD, BMCS, Wavelet Analysis, LBI, Topographic Approach and MCW. Results show that our method achieves better results for circumscribed, spiculated lesions and ill-defined lesions, considering the similarity between segmentation results and ground-truth images. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Breast cancer Mass detection Mammography image analysis Biomedical image segmentation growcut algorithm

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A semi-supervised fuzzy growcut algorithm to segment and classify regions of interest of mammographic images

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EXPERT SYSTEMS WITH APPLICATIONS 2016年 65卷 116-126页

作者： Cordeiro, Filipe R. Santos, Wellington P. Silva-Filho, Abel G. Univ Fed Pernambuco Informat Ctr Recife PE Brazil Univ Fed Pernambuco Dept Biomed Engn Recife PE Brazil

According to the World Health Organization, breast cancer is the most common form of cancer in women. It is the second leading cause of death among women round the world, becoming the most fatal form of cancer. Despite the existence of several imaging techniques useful to aid at the diagnosis of breast cancer, x-ray mammography is still the most used and effective imaging technology. Consequently, mammographic image segmentation is a fundamental task to support image analysis and diagnosis, taking into account shape analysis of mammary lesions and their borders. However, mammogram segmentation is a very hard process, once it is highly dependent on the types of mammary tissues. The growcut algorithm is a relatively new method to perform general image segmentation based on the selection of just a few points inside and outside the region of interest, reaching good results at difficult segmentation cases when these points are correctly selected. In this work we present a new semi-supervised segmentation algorithm based on the modification of the growcut algorithm to perform automatic mammographic image segmentation once a region of interest is selected by a specialist. In our proposal, we used fuzzy Gaussian membership functions to modify the evolution rule of the original growcut algorithm, in order to estimate the uncertainty of a pixel being object or background. The main impact of the proposed method is the significant reduction of expert effort in the initialization of seed points of growcut to perform accurate segmentation, once it removes the need of selection of background seeds. Furthermore, the proposed method is robust to wrong seed positioning and can be extended to other seed based techniques. These characteristics have impact on expert and intelligent systems, once it helps to develop a segmentation method with lower required specialist knowledge, being robust and as efficient as state of the art techniques. We also constructed an automatic point selection

关键词： Breast cancer Mammographic image analysis Semi-supervised image segmentation growcut algorithm Fuzzy segmentation Simulated annealing

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Mammography Lesion Detection Using an Improved growcut algorithm 25

Mammography Lesion Detection Using an Improved GrowCut Algor...

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25th KES International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES)

作者： Moroz-Dubenco, Cristiana Diosan, Laura Andreica, Anca Babes Bolyai Univ Dept Comp Sci Mihail Kogalniceanu 1 Cluj Napoca 400084 Romania

Breast cancer is one of the most common types of cancer amongst women, but it is also one of the most frequently cured cancers. Because of this, early detection is crucial, and this can be done through mammography screening. With the increasing need of an automated interpretation system, a lot of methods have been proposed so far and, regardless of the algorithms, they all share a step: segmentation. That is, identifying the region of interest in order to further analyze and classify it either as benign or malignant. However, due to the different types of mammary tissues, mammography segmentation can prove to be a difficult task. Various techniques of mammography segmentation have been proposed so far. Yet, since obtaining the ground-truth for mammographic images might be problematic, recent literature leans towards unsupervised techniques. In this paper we present a segmentation approach based on the growcut algorithm. The original method starts with a number of seed points inside and outside the region of interest, selected by a human expert, and iterates over the pixels multiple times, until it reaches a stable state where all the pixels have been assigned to a class. Our proposal aims to reduce the human intervention by eliminating the need of selecting initial background seeds and, also, to reduce the computational time by limiting the process of parsing the entire image to a small, fixed number of iterations. The proposed approach was compared to the original method, using three variants: (1) automatically constructing the initial background seeds surrounding the foreground ones;(2) using mammograms's background as initial background seeds;(3) not using initial background seeds. Experimental results obtained for the Mini-MIAS dataset show that, for the variations that use background seeds outside the breast and that do not use any background seeds, our approach yields much better results than the original method. (C) 2021 The Authors. Published by Elsevier B.V

关键词： image segmentation breast cancer mammographic image analysis mass detection growcut algorithm

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An Unsupervised Threshold-based growcut algorithm for Mammography Lesion Detection

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Procedia Computer Science 2022年 207卷 2096-2105页

作者： Cristiana Moroz-Dubenco Adél Bajcsi Anca Andreica Camelia Chira Department of Computer Science Babeş -Bolyai University Mihail Kogălniceanu 1 400084 Cluj-Napoca Romania

Breast cancer causes numerous deaths worldwide; yet the numbers have decreased in the past years as a result of computer-aided diagnosis and proper treatment. The current paper is addressed to the base of such diagnosis system: pre-processing and segmentation. After a robust pre-processing, an unsupervised version of growcut is applied to define the location of the abnormality. We present a method to automatically define the foreground seeds used in growcut. For experiments, mammograms from mini-MIAS dataset are used and a precision of 93.63% for the foreground seeds masks is achieved, which leads to promising segmentation results.

关键词： image segmentation breast cancer mammographic image analysis mass detection growcut algorithm

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Mammography Lesion Detection Using an Improved growcut algorithm

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Procedia Computer Science 2021年 192卷 308-317页

作者： Cristiana Moroz-Dubenco Laura Dioşan Anca Andreica Department of Computer Science Babeş-Bolyai University Mihail Kogălniceanu 1 400084 Cluj-Napoca Romania

Breast cancer is one of the most common types of cancer amongst women, but it is also one of the most frequently cured cancers. Because of this, early detection is crucial, and this can be done through mammography screening. With the increasing need of an automated interpretation system, a lot of methods have been proposed so far and, regardless of the algorithms, they all share a step: segmentation. That is, identifying the region of interest in order to further analyze and classify it either as benign or malignant. However, due to the different types of mammary tissues, mammography segmentation can prove to be a difficult task. Various techniques of mammography segmentation have been proposed so far. Yet, since obtaining the ground-truth for mam-mographic images might be problematic, recent literature leans towards unsupervised techniques. In this paper we present a segmentation approach based on the growcut algorithm. The original method starts with a number of seed points inside and outside the region of interest, selected by a human expert, and iterates over the pixels multiple times, until it reaches a stable state where all the pixels have been assigned to a class. Our proposal aims to reduce the human intervention by eliminating the need of selecting initial background seeds and, also, to reduce the computational time by limiting the process of parsing the entire image to a small, fixed number of iterations. The proposed approach was compared to the original method, using three variants: (1) automatically constructing the initial background seeds surrounding the foreground ones; (2) using mammograms’s background as initial background seeds; (3) not using initial background seeds. Experimental results obtained for the Mini-MIAS dataset show that, for the variations that use background seeds outside the breast and that do not use any background seeds, our approach yields much better results than the original method.

关键词： image segmentation breast cancer mammographic image analysis mass detection growcut algorithm

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Diagnosis support system for skin grafting using growcut with an automated seed selection

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COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING-IMAGING AND VISUALIZATION 2023年第3期11卷 942-959页

作者： Ezaki, Kodai Kato, Soichiro Yamaguchi, Yoshihiro Tanaka, Toshiyuki Keio Univ Fac Sci & Technol Yokohama Kanagawa Japan Kyorin Univ Sch Med Dept Trauma & Crit Care Med Tokyo Japan

This paper deals with a method of segmenting skin and skin grafts using growcut. The previous lacks system usability because the evaluating physician needed approximately 100-200 mouse clicks for each image to acquire training data. Therefore, we propose a method to automate the acquisition of training data while maintaining the accuracy of domain segmentation from the previous study. Based on the proposed method, the ratio of the two areas-of-interest was calculated as the burn ratio and used to evaluate treatment quantitatively. The results show that the segmentation of skin and skin grafts is as accurate as by mouse clicks. We think that automating the acquisition of training data as preprocessing will improve the usability of the system.

关键词： Skin grafting growcut algorithm automated seed selection

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A new approach to diagnosing prostate cancer through magnetic resonance imaging

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ALEXANDRIA ENGINEERING JOURNAL 2021年第1期60卷 897-904页

作者： Zhang, Li Li, Longchao Tang, Min Huan, Yi Zhang, Xiaoling Zhe, Xia Fourth Mil Med Univ Xijing Hosp Dept Radiol Xian 710032 Peoples R China Shaanxi Prov Peoples Hosp Dept MRI Xian 710068 Peoples R China

This paper combines improved growcut and Zernik feature extraction and ensemble learning techniques such as KNN, SVM, and MLP algorithms to prostate cancer detection and lesion segmentation in MRI. We use improved growcut algorithm for segmentation of the suspected cancer area and the combination of machine learning algorithms such as KNN, SVM, and MLP in the ensemble learning system to detect prostate cancer. We found that the accuracy of this method, which is a combination of several methods, improved by about 20% compared to other methods. Other metrics such as precision, recall, and error of proposed method have been improved. (C) 2020 The Authors. Published by Elsevier B.V. on behalf of Faculty of Engineering, Alexandria University.

关键词： Prostate cancer Ensemble learning Zernik feature extraction growcut algorithm

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Seismically induced soft-sediment deformation structures revealed by X-ray computed tomography of boring cores

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TECTONOPHYSICS 2016年 683卷 138-147页

作者： Nakashima, Yoshito Komatsubara, Junko Geol Survey Japan Natl Inst Adv Ind Sci & Technol AIST Cent 7Higashi 1-1-1 Tsukuba Ibaraki 3058567 Japan

X-ray computed tomography (CT) allows us to visualize three-dimensional structures hidden in boring cores nondestructively. We applied medical X-ray CT to cores containing seismically induced soft-sediment deformation structures (SSDSs) obtained from the Kanto region of Japan, where the 2011 off the Pacific coast of Tohoku Earthquake occurred. The CT images obtained clearly revealed various types of the seismically induced SSDSs embedded in the cores: a propagating sand dyke bent complexly by the preexisting geological structure, deformed laminations of fluidized sandy layers, and two types of downward mass movement (ductile downward folding and brittle normal faulting) as compensation for upward sand transport through sand dykes. Two advanced image analysis techniques were applied to the sand dyke CT images for the first time. The growcut algorithm, a specific digital image segmentation technique that uses cellular automata, was used successfully to extract the three-dimensional complex sand dyke structures embedded in the sandy sediments, which would have been difficult to achieve using a conventional image processing technique. Local autocorrelation image analysis was performed to detect the flow pattern aligned along the sand dykes objectively. The results demonstrate that X-ray CT coupled with advanced digital image analysis techniques is a promising approach to studying the seismically induced SSDSs in boring cores. (C) 2016 The Authors. Published by Elsevier B.V.

关键词： 2011 Tohoku earthquake Boring cores growcut algorithm Sand dyke Seismically induced soft-sediment deformation X-ray CT

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