Breast Tissue 3D Segmentation and Visualization on MRI

Tissue segmentation and visualization are useful for breast lesion detection and quantitative analysis. In this paper, a 3D segmentation algorithm based on Kernel-based Fuzzy C-Means (KFCM) is proposed to separate the breast MR images into different tissues. Then, an improved volume rendering algorithm based on a new transfer function model is applied to implement 3D breast visualization. Experimental results have been shown visually and have achieved reasonable consistency.

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Grammatical Fireworks Algorithm Method for Breast Lesion Segmentation in DCE-MR Images

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.g9054.0510721 ◽

2021 ◽

Vol 10 (7) ◽

pp. 170-182

Author(s):

D. K. Patra* ◽

S. Mondal ◽

P. Mukherjee

Keyword(s):

Tissue Characterization ◽

Lesion Detection ◽

Mr Images ◽

Dce Mri ◽

Fireworks Algorithm ◽

Clustering Technique ◽

Analysis Task ◽

Breast Mr ◽

Segmented Images ◽

Experimental Findings

For cancer detection and tissue characterization, DCE-MRI segmentation and lesion detection is a critical image analysis task. To segment breast MR images for lesion detection, a hard-clustering technique with Grammatical Fireworks algorithm (GFWA) is proposed in this paper. GFWA is a Swarm Programming (SP) system for automatically generating computer programs in any language. GFWA is used to create the cluster core for clustering the breast MR images in this article. The presence of noise and intensity inhomogeneities in MR images complicates the segmentation process. As a result, the MR images are denoised at the start, and strength inhomogeneities are corrected in the preprocessing stage. The proposed GFWA-based clustering technique is used to segment the preprocessed MR images. Finally, from the segmented images, the lesions are removed. The proposed approach is tested on 5 patients’ 25 DCE-MRI slices. The proposed method’s experimental findings are compared to those of the Grammatical Swarm (GS)-based clustering technique and the K-means algorithm. The proposed method outperforms other approaches in terms of both quantitative and qualitative results.

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Breast Tissue Segmentation Using KFCM Algorithm on MR images

Lecture Notes in Electrical Engineering - Proceedings of 2013 Chinese Intelligent Automation Conference ◽

10.1007/978-3-642-38466-0_62 ◽

2013 ◽

pp. 555-563 ◽

Cited By ~ 3

Author(s):

Hong Song ◽

Feifei Sun ◽

Xiangfei Cui ◽

Xiangbin Zhu ◽

Qingjie Zhao

Keyword(s):

Breast Tissue ◽

Mr Images ◽

Tissue Segmentation

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Spectrum loft method for fibro-glandular tissue segmentation in pre-contrast breast MR images

2017 Fourth International Conference on Image Information Processing (ICIIP) ◽

10.1109/iciip.2017.8313722 ◽

2017 ◽

Author(s):

Prajval Koul ◽

Vineeta Kumari ◽

Gyanendra Sheoran

Keyword(s):

Glandular Tissue ◽

Mr Images ◽

Tissue Segmentation ◽

Breast Mr

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Evaluation of a Coherent Point Drift Algorithm for Breast Image Registration via Surface Markers

Journal of Computational Vision and Imaging Systems ◽

10.15353/vsnl.v2i1.90 ◽

2016 ◽

Vol 2 (1) ◽

Author(s):

Ghazaleh Ahmadian ◽

C. Sean Bohun ◽

Mehran Ebrahimi

Keyword(s):

Breast Tissue ◽

Breast Magnetic Resonance Imaging ◽

Three Dimensional ◽

Surface Markers ◽

Mr Images ◽

Breast Image ◽

Breast Mr ◽

Coherent Point Drift ◽

Breast Magnetic Resonance ◽

Magnetic Resonance Imaging Mri

Breast Magnetic Resonance Imaging (MRI) is a reliable imagingtool for localization and evaluation of lesions prior to breast conservingsurgery (BCS). MR images typically will be used to determinethe size and location of the tumours before making the incisionin order to minimize the amount of tissue excised.The arm position and configuration of the breast during andprior to surgery are different and one question is whether it wouldbe possible to match the two configurations. This matching processcan potentially be used in development of tools to guide surgeonsin the incision process.Recently, a Thin-Plate-Spline (TPS) algorithm has been proposedto assess the feasibility of breast tissue matching using fiducialsurface markers in two different arm positions. The registrationalgorithm uses the surface markers only and does not employ theimage intensities.In this manuscript, we apply and evaluate a coherent point drift(CPD) algorithm for registration of three-dimensional breast MR imagesof six patient volunteers. In particular, we evaluate the resultsof the previous TPS registration technique to the proposed rigidCPD, affine CPD, and deformable CPD registration algorithms onthe same patient datasets.The preliminary results suggest that the CPD deformable registrationalgorithm is superior in correcting the motion of the breastcompared to CPD rigid, affine and TPS registration algorithms.

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