Towards optimal cropping: breast and liver tumor classification using ultrasound images

2021 ◽  
Author(s):  
Tahir Hassan ◽  
Alaa Alzoubi ◽  
Hongbo Du ◽  
Sabah Jassim
Keyword(s):  
Liver Tumor ◽  
Tumor Classification ◽  
Ultrasound Images ◽  
Optimal Cropping

Ultrasound liver tumor segmentation using adaptively regularized kernel-based fuzzy C means with enhanced level set algorithm

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Deepak S. Uplaonkar ◽  
Virupakshappa ◽  
Nagabhushan Patil
Keyword(s):  
Level Set ◽  
Liver Tumor ◽  
Experimental Analysis ◽  
Tumor Segmentation ◽  
Ultrasound Images ◽  
Dice Coefficient ◽  
Content Type ◽  
Fuzzy C Means ◽  
Local Ternary Pattern ◽  
Liver Tumor Segmentation

PurposeThe purpose of this study is to develop a hybrid algorithm for segmenting tumor from ultrasound images of the liver.Design/methodology/approachAfter collecting the ultrasound images, contrast-limited adaptive histogram equalization approach (CLAHE) is applied as preprocessing, in order to enhance the visual quality of the images that helps in better segmentation. Then, adaptively regularized kernel-based fuzzy C means (ARKFCM) is used to segment tumor from the enhanced image along with local ternary pattern combined with selective level set approaches.FindingsThe proposed segmentation algorithm precisely segments the tumor portions from the enhanced images with lower computation cost. The proposed segmentation algorithm is compared with the existing algorithms and ground truth values in terms of Jaccard coefficient, dice coefficient, precision, Matthews correlation coefficient, f-score and accuracy. The experimental analysis shows that the proposed algorithm achieved 99.18% of accuracy and 92.17% of f-score value, which is better than the existing algorithms.Practical implicationsFrom the experimental analysis, the proposed ARKFCM with enhanced level set algorithm obtained better performance in ultrasound liver tumor segmentation related to graph-based algorithm. However, the proposed algorithm showed 3.11% improvement in dice coefficient compared to graph-based algorithm.Originality/valueThe image preprocessing is carried out using CLAHE algorithm. The preprocessed image is segmented by employing selective level set model and Local Ternary Pattern in ARKFCM algorithm. In this research, the proposed algorithm has advantages such as independence of clustering parameters, robustness in preserving the image details and optimal in finding the threshold value that effectively reduces the computational cost.

FMRNet: A fused network of multiple tumoral‐regions for breast tumor classification with ultrasound images

2021 ◽  
Author(s):  
Wenju Cui ◽  
Yunsong Peng ◽  
Gang Yuan ◽  
Weiwei Cao ◽  
Yuzhu Cao ◽  
...  
Keyword(s):  
Breast Tumor ◽  
Tumor Classification ◽  
Ultrasound Images ◽  
Breast Tumor Classification

Breast Tumor Classification of Ultrasound Images Using Wavelet-Based Channel Energy and ImageJ

2009 ◽  
Vol 3 (1) ◽  
pp. 81-93 ◽  
Author(s):  
Hsieh-Wei Lee ◽  
Bin-Da Liu ◽  
King-Chu Hung ◽  
Sheau-Fang Lei ◽  
Po-Chin Wang ◽  
...  
Keyword(s):  
Breast Tumor ◽  
Tumor Classification ◽  
Ultrasound Images ◽  
Breast Tumor Classification

scholarly journals Breast tumor classification in ultrasound images using support vector machines and neural networks

2016 ◽  
Vol 32 (3) ◽  
pp. 283-292 ◽  
Author(s):  
Carmina Dessana Lima Nascimento ◽  
Sérgio Deodoro de Souza Silva ◽  
Thales Araújo da Silva ◽  
Wagner Coelho de Albuquerque Pereira ◽  
Marly Guimarães Fernandes Costa ◽  
...  
Keyword(s):  
Neural Networks ◽  
Support Vector Machines ◽  
Breast Tumor ◽  
Tumor Classification ◽  
Support Vector ◽  
Ultrasound Images ◽  
Vector Machines ◽  
Breast Tumor Classification
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