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

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.

Segmentation of Brain MRI Using Wavelet Transform and Grammatical Bee Colony

2018 ◽  
Vol 27 (07) ◽  
pp. 1850108 ◽  
Author(s):  
Tapas Si ◽  
Arunava De ◽  
Anup Kumar Bhattacharjee
Keyword(s):  
Magnetic Resonance ◽  
Brain Mri ◽  
Mr Images ◽  
Bee Colony ◽  
Analysis Task ◽  
Trained Classifier ◽  
Skull Stripping ◽  
Segmented Images ◽  
Fluid Attenuated Inversion Recovery ◽  
Magnetic Resonance Imaging Mri

Multimodal Magnetic Resonance Imaging (MRI) is an imaging technique widely used in the diagnosis and treatment planning of patients. Lesion segmentation of brain MRI is one of the most important image analysis task in medical imaging. In this paper, a new method for the supervised segmentation of the lesion in brain MRI using Grammatical Bee Colony (GBC) is proposed. The segmentation process is adversely affected by the presence of noises and intensity inhomogeneities in the Magnetic Resonance (MR) images. Therefore, noises are removed from the images and intensity inhomogeneities are corrected in the pre-processing steps. A set of stationary wavelet features are extracted from the co-registered [Formula: see text]1-weighted ([Formula: see text]-[Formula: see text]), [Formula: see text]2-weighted ([Formula: see text]-[Formula: see text]) and Fluid–Attenuated Inversion Recovery (FLAIR) images after skull stripping. A classifier is evolved using the GBC to classify the tissues as healthy tissues or lesions. The GBC classifier is trained with extracted features. The trained classifier is used to segment the test Magnetic Resonance (MR) image into healthy tissues or lesion regions. Finally, the connected component labeling algorithm is used to extract the lesions from the segmented images in the post-processing step. Effectiveness of the proposed method is tested by identifying the brain lesions from a set of MR images.

Spatial based breast MR images lesion detection and colourisation

2010 ◽  
Author(s):  
K.S. Sim ◽  
F.K. Chia ◽  
S.S. Chong ◽  
C.P. Tso ◽  
Siti Fathimah Abbas ◽  
...  
Keyword(s):  
Lesion Detection ◽  
Mr Images ◽  
Breast Mr

TU-D-330A-01: Computerized Lesion Detection On Breast MR Images

2006 ◽  
Vol 33 (6Part17) ◽  
pp. 2195-2195 ◽  
Author(s):  
J Bian ◽  
W Chen ◽  
G Newstead ◽  
M Giger
Keyword(s):  
Lesion Detection ◽  
Mr Images ◽  
Breast Mr

scholarly journals Breast Tissue 3D Segmentation and Visualization on MRI

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Hong Song ◽  
Xiangfei Cui ◽  
Feifei Sun
Keyword(s):  
Volume Rendering ◽  
Breast Tissue ◽  
Breast Lesion ◽  
Lesion Detection ◽  
Mr Images ◽  
3D Segmentation ◽  
Tissue Segmentation ◽  
Function Model ◽  
Breast Mr ◽  
Different Tissues

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.

Dysprosium-enhanced MR imaging for tumor tissue characterization

1997 ◽  
Vol 38 (2) ◽  
pp. 281-286 ◽  
Author(s):  
C. Wang ◽  
A. Sundin ◽  
A. Ericsson ◽  
T. Bach-Gansmo ◽  
A. Hemmingsson ◽  
...  
Keyword(s):  
Mr Imaging ◽  
Tissue Characterization ◽  
Tumor Tissue ◽  
Tumor Model ◽  
Human Colon ◽  
Mr Images ◽  
Histological Sections ◽  
Necrotic Tumor ◽  
Before And After ◽  
Weighted Sequences

Purpose: to evaluate dysprosium-enhanced MR imaging for differentiation between morphologically intact and necrotic tumor tissue in a tumor model. Material and Methods: A human colon carcinoma was transplanted subcutaneously into 9 nude (immunodeprived) rats. MR imaging was performed before and after injection of the dysprosium agent Dy-DTPA-BMA. T1-, T2- and T2*-weighted sequences were acquired. the tumors were dissected, histological sections were prepared, and compared with corresponding MR images. Results: in intact tissue, the MR signal intensity in the T2- and T2*-weighted images decreased after Dy injection and the delineation of the intact regions were sharp and corresponded well to the gross histological sections. Conclusion: Dy-enhanced MR imaging facilitated the differentiation between intact and necrotic tumor tissue.

Dynamic breast MR imaging: is parametric mapping superior to image subtraction in lesion detection?

2007 ◽  
Vol 17 (12) ◽  
pp. 3093-3099 ◽  
Author(s):  
Kathinka D. Kurz ◽  
Hans-Jörg Wittsack ◽  
Reinhart Willers ◽  
Dirk Blondin ◽  
Ulrich Mödder ◽  
...  
Keyword(s):  
Mr Imaging ◽  
Lesion Detection ◽  
Image Subtraction ◽  
Parametric Mapping ◽  
Breast Mr

scholarly journals Segmentation of breast MR images based on multiresolution level set algorithm

2014 ◽  
Vol 63 (11) ◽  
pp. 118701
Author(s):  
Fan Hong ◽  
Zhu Yan-Chun ◽  
Wang Fang-Mei ◽  
Zhang Xu-Mei
Keyword(s):  
Level Set ◽  
Mr Images ◽  
Breast Mr

scholarly journals Automated Detection and Segmentation of Nonmass-Enhancing Breast Tumors with Dynamic Contrast-Enhanced Magnetic Resonance Imaging

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Ignacio Alvarez Illan ◽  
Javier Ramirez ◽  
J. M. Gorriz ◽  
Maria Adele Marino ◽  
Daly Avendano ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
False Positive Rate ◽  
Lesion Detection ◽  
Support Vector ◽  
Breast Cancer Patients ◽  
Resonance Imaging ◽  
Dce Mri ◽  
Dynamic Contrast Enhanced ◽  
Contrast Enhanced

Nonmass-enhancing (NME) lesions constitute a diagnostic challenge in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) of the breast. Computer-aided diagnosis (CAD) systems provide physicians with advanced tools for analysis, assessment, and evaluation that have a significant impact on the diagnostic performance. Here, we propose a new approach to address the challenge of NME lesion detection and segmentation, taking advantage of independent component analysis (ICA) to extract data-driven dynamic lesion characterizations. A set of independent sources was obtained from the DCE-MRI dataset of breast cancer patients, and the dynamic behavior of the different tissues was described by multiple dynamic curves, together with a set of eigenimages describing the scores for each voxel. A new test image is projected onto the independent source space using the unmixing matrix, and each voxel is classified by a support vector machine (SVM) that has already been trained with manually delineated data. A solution to the high false-positive rate problem is proposed by controlling the SVM hyperplane location, outperforming previously published approaches.

Sign in / Sign up

Export Citation Format

Share Document