Research on Fuzzy Enhancement in the Diagnosis of Liver Tumor from B-mode Ultrasound Images

2011 ◽  
Author(s):  
Wu Qiu ◽  
Rui Wang ◽  
Feng Xiao ◽  
Mingyue Ding
Keyword(s):  
Liver Tumor ◽  
Ultrasound Images ◽  
Fuzzy Enhancement

scholarly journals Research on Fuzzy Enhancement in the Diagnosis of Liver Tumor from B-mode Ultrasound Images

2011 ◽  
Vol 3 (3) ◽  
pp. 10-16 ◽  
Author(s):  
Qiu Wu ◽  
Feng Xiao ◽  
Xin Yang ◽  
Xuming Zhang ◽  
Yuchi Ming ◽  
...  
Keyword(s):  
Liver Tumor ◽  
Ultrasound Images ◽  
Fuzzy Enhancement

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.

An Accurate Detection Method for Biopsy Needle in Ultrasound Images Based on Fuzzy Enhancement and Hough Transform

2019 ◽  
Vol 9 (7) ◽  
pp. 1505-1509
Author(s):  
Jinxia Ren ◽  
Pavel Shcherbakov ◽  
Xiaoping Zhang ◽  
Mingyue Ding ◽  
Huageng Liang ◽  
...  
Keyword(s):  
Hough Transform ◽  
Detection Method ◽  
Ultrasound Images ◽  
Biopsy Needle ◽  
Fuzzy Enhancement ◽  
Accurate Detection

scholarly journals Ketogenic Diet Against Liver Tumor

2020 ◽  
Vol 4 (Supplement_2) ◽  
pp. 338-338
Author(s):  
Jacquelyne Motta ◽  
Luciano Lobo ◽  
Donadao Fabiola
Keyword(s):  
Blood Glucose ◽  
Side Effects ◽  
Ketogenic Diet ◽  
Liver Tumor ◽  
Case Reports ◽  
The Body ◽  
Ultrasound Images ◽  
Alternative Source ◽  
Glucose Levels ◽  
Funding Sources

Abstract Objectives Report my experience in treating two liver tumor patients with ketogenic diet KD protocol as monotherapy. In addition to the two patients who enrolled in this study, I also reviewed findings from 10 other patients, including 2 patients from case reports. Methods The ketogenic diet consists of high fat foods, foods that contain an adequate amount of protein, and a very low amount of carbohydrates. Normally, the body gets its main source of energy (sugar) from carbs. However, the ketogenic diet deprives the body of glucose, inducing a state of “ketosis.” During ketosis, the body is forced to break down stored fat instead of sugar to produce an alternative source of energy. Results The two patients who enrolled in my KD pilot study were monitored with twice daily measurements of blood glucose and ketones. Within 30 days of initiating the KD blood glucose levels declined to low-normal levels and blood ketones were elevated. Results of ultrasound images indicated that tumors has gone 1 year after KD. One patient exhibited significant clinical improvements during the study. All of these patients was treated using KD as monotherapy. No major side effects due to KD have been reported in any of these patients. They continued the ketogenic diet remaining free of disease progression. Conclusions I conclude that: 1. KD is safe and without major side effects; 2. ketosis can be induced using customary foods; 3. treatment with KD may be effective in controlling the progression of some liver cancer cells; 4. Maybe we can manipulate our own biological system a little bit or activate something we already have in place in order to more effectively combat cancer. Funding Sources DoTerra Science.

Application of Fuzzy Enhancement in the Diagnosis of Liver Cancer from Ultrasound Images

2012 ◽  
Vol 195-196 ◽  
pp. 493-497
Author(s):  
Wu Qiu ◽  
Feng Xiao ◽  
Xin Yang ◽  
Mao Lin Ye ◽  
Yu Chi Ming ◽  
...  
Keyword(s):  
Liver Cancer ◽  
Back Propagation ◽  
Normal Liver ◽  
Computer Aided Diagnosis ◽  
Support Vector ◽  
Ultrasound Images ◽  
Gray Level ◽  
Fuzzy Enhancement ◽  
Computer Aided ◽  
Aided Diagnosis

Fuzzy enhancement is applied in computer aided diagnosis of liver cancer from B mode ultrasound images as a pre-processing procedure in this paper. It was evaluated with three classifiers including K means, back propagation neural network and support vector machine using 25 features from single gray-level statistic, gray-level co-occurrence matrix (GLCM), and gray-level run-length matrix (GLRLM). The results show that the fuzzy enhancement algorithm can improve classification accuracy of normal liver, liver cancer and Hemangioma from B mode ultrasound images for three classifiers. It is proved that fuzzy enhancement as an efficient preprocessing procedure could be used in the computer aided diagnosis system of liver cancer.

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