NDLI: Hybrid segmentation method based on multi-scale Gaussian kernel fuzzy clustering with spatial bias correction and region-scalable fitting for breast US images

Content Provider	IET Digital Library
Author	Panigrahi, Lipismita Verma, Kesari Singh, Bikesh Kumar
Abstract	Automated segmentation of tumors in breast ultrasound (US) images is challenging due to poor image quality, presence of speckle noise, shadowing effects and acoustic enhancement. This paper improves the multi-scale Gaussian kernel induced fuzzy C-means clustering method with spatial bias correction (MsGKFCM_S). Furthermore, it presents a hybrid segmentation method, using both the features of the MsGKFCM_S clustering and active contour driven by a region-scalable fitting energy function. The result obtained from the MsGKFCM_S method is utilised to initialise the contour that spreads to identify the estimated regions. It also helps to estimate the several controlling parameters of the curve evolution process. The proposed approach is evaluated on a database of 127 breast US images consisting of 75 malignant and 52 solid benign cases. The performance of proposed approach is compared with other related techniques, using performance measures such as Jaccard Index, dice similarity, shape similarity, Hausdroff difference, area difference, accuracy and F-measure. Results indicate that the proposed approach can successfully detect lesions in breast US images, with high accuracy of 97.889 and 97.513%. Moreover, the proposed approach has the capability of handling shadowing effects, acoustic enhancement and multiple lesions.
Starting Page	1067
Ending Page	1077
Page Count	11
ISSN	17519632
Volume Number	12
e-ISSN	17519640
Issue Number	Issue 8, Dec (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-cvi/12/8
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-cvi.2018.5332
Journal	IET Computer Vision
Publisher Date	2018-10-02
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Acoustic Enhancement Active Contour Algebra Automated Tumour Segmentation Biology And Medical Computing Biomedical Ultrasonics Breast Ultrasound Image Breast US Image Combinatorial Mathematics Computer Vision And Image Processing Technique Controlling Parameter Estimation Curve Evolution Process Data Handling Technique Dice Similarity Estimated Region F-measure Fuzzy Set Theory Gaussian Processes Graph Theory Hausdroff Difference Hybrid Segmentation Method Image Quality Image Segmentation Jaccard Index Lesion Detection Medical Image Processing MsGKFCM_S Clustering Method Multiscale Gaussian Kernel-induced FCM Multiscale Gaussian Kernel-induced Fuzzy C-means Clustering Method Optical, Image And Video Signal Processing Patient Diagnostic Method And Instrumentation Pattern Clustering Probability Theory Region-scalable Fitting Energy Function Set Theory Shadowing Effect Shape Similarity Sonic And Ultrasonic Radiation Spatial Bias Correction Speckle Speckle Noise Statistics Stochastic Linearised SCUC Tumours
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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