A spatial fuzzy based level set method for mammogram mass segmentation

Anitha, J.; Peter, J. Dinesh

doi:10.1109/ecs.2015.7194713

Cited by 6 publications

(4 citation statements)

References 17 publications

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“…Fuzzy theory and fuzzy logic are well-known to be flexible tools where imprecise knowledge or not-well defined features have to be used [17]. It has been extensively used in system control domain and applied in image processing to some extent [18][19][20][21]. In the past 10 years, thresholding segmentation approaches have been proposed based on fuzzy logic and fuzzy calculations.…”

Section: Related Workmentioning

confidence: 99%

Fuzzy Multilevel Image Thresholding Based on Modified Quick Artificial Bee Colony Algorithm and Local Information Aggregation

Sun

Guo

et al. 2016

Mathematical Problems in Engineering

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Thresholding segmentation based on fuzzy entropy and intelligent optimization is one of the most commonly used and direct methods. This paper takes fuzzy Kapur's entropy as the best optimal objective function, with modified quick artificial bee colony algorithm (MQABC) as the tool, performs fuzzy membership initialization operations through Pseudo Trapezoid-Shaped (PTS) membership function, and finally, according to the image's spacial location information, conducts local information aggregation by way of median, average, and iterative average so as to achieve the final segmentation. The experimental results show that the proposed FMQABC (fuzzy based modified quick artificial bee colony algorithm) and FMQABCA (fuzzy based modified quick artificial bee colony and aggregation algorithm) can search out the best optimal threshold very effectively, precisely, and speedily and in particular show exciting efficiency in running time. This paper experimentally compares the proposed method with Kapur's entropy-based Electromagnetism Optimization (EMO) method, standard ABC, and FDE (fuzzy entropy based differential evolution algorithm), respectively, and concludes that MQABCA is far more superior to the rest in terms of segmentation quality, iterations to convergence, and running time.

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Section: Related Workmentioning

confidence: 99%

Fuzzy Multilevel Image Thresholding Based on Modified Quick Artificial Bee Colony Algorithm and Local Information Aggregation

Sun

Guo

et al. 2016

Mathematical Problems in Engineering

View full text Add to dashboard Cite

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“…After scanning the body, the radiologist will analyze the medical image to get the diagnosis. The analysis process can run using the help of computer programs to speed up and improve the accuracy of the diagnostic process [3]. In the field of medical image analysis on bioinformatics science, one of the computational processes that can always be improved to help this work is image segmentation [4].…”

Section: Introductionmentioning

confidence: 99%

“…If in previous studies [9] fuzzy logic with spatial information applied to brain MRI imagery, Dr. J. Dinesh Peter applied to the image of the mammogram used for the story of breast cancer [3]. The study utilized mammogram image data from mini-MIAS database then comparison was done between standard method of FCM and FCM with new spatial information.…”

Section: Introductionmentioning

confidence: 99%

Breast Cancer Image Segmentation Using K-Means Clustering Based on GPU Cuda Parallel Computing

Amalia¹,

Airlangga²,

Thohari³

2018

INFOTEL

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Utilization of image processing technology is now widely used in the health of one of them to help radiologist to analyze MRI (Magnetic Resonance Imaging), CT Scan and Mammography. Image segmentation is a process intended to get the objects contained in the image by dividing the image into several areas that have similarity attributes on an object with the aim of facilitating the analysis process. The amount of data available from each patient is increasing and the larger the image size is a new challenge to keep the process accurate but can run fast. Research on the segmentation of medical images have been done but still few that combine with parallel computing. In this research, the implementation of K-Means clustering on the image of mammography result using two way computation that is serial and parallel. The results show that parallel computing gives average performance execution time faster up to twice.

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“…D. Pereira et al [7], implemented an artifact removal algorithm in CC and MLO views, the wavelet transform and Wiener filter are used for image enhancement, finally, the authors employed multiple thresholding wavelet transform and genetic algorithm for masses detection and segmentation. J. Anitha et al [8], proposed spatial based fuzzy level set algorithm for automatic mammogram mass segmentation, the output of fuzzy clustering is used as input for the level set segmentation that is used to refine the mass boundary, the results of fuzzy clustering are used to estimate the control parameters of level set algorithm. Khaddouj Taifi et al [9], proposed a hybrid technique for enhancing mammograms, this technique combines Nonsubsampled Contourlet Transform and Hommomorphic Filtering, then the authors presented a comparative study based on three different algorithms: proposed methods, homomorphic filtering and unsharp masking, the experimental results of this study show that the proposed method significantly reduces noise in high noise mammograms.…”

Section: Introductionmentioning

confidence: 99%

Breast Mass Segmentation Using a Semi-automatic Procedure Based on Fuzzy C-means Clustering

Saleck¹,

Moutaouakkil²,

Mouçouf³

et al. 2018

TELKOMNIKA

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Mammography is the primary modality that helped in the early detection and diagnosis of women b reast diseases. Further, the process of extracting the masses in mammogram represents a challenging task facing the radiologists, due to prob lems such as fuzzy or speculated b orders, low contrast and the presence of intensity inhomogeneities. Aims to help the radiologists in the diagnosis of b reast cancer, many approaches have b een conducted to automatically segment the masses in mammograms. Towards this aim, in this paper, we present a new approach for extraction of tumors from region-of-interest (ROI) using the algorithm of Fuzzy C-Means (FCM) setting two clusters for semi-automated segmentation. The proposed method meant to select as input data the set of pixels that enab le to get the meaningful information required to segment the masses with high accuracy. This could b e accomplished through eliminating unnecessary pixels, which influence on this process through separating it outside of the input data using an optimal thresho ld given b y monitoring the change of clusters rate during the process of threshold decrementing. The proposed methodology has successfully segmented the masses, with an average sensitivity of 82.02% and specificity of 98.23%.

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A spatial fuzzy based level set method for mammogram mass segmentation

Cited by 6 publications

References 17 publications

Fuzzy Multilevel Image Thresholding Based on Modified Quick Artificial Bee Colony Algorithm and Local Information Aggregation

Fuzzy Multilevel Image Thresholding Based on Modified Quick Artificial Bee Colony Algorithm and Local Information Aggregation

Breast Cancer Image Segmentation Using K-Means Clustering Based on GPU Cuda Parallel Computing

Breast Mass Segmentation Using a Semi-automatic Procedure Based on Fuzzy C-means Clustering

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