Proceedings ArticleDOI
Liver tumor diagnosis by gray level and contourlet coefficients texture analysis
S. S. Kumar,R. S. Moni,J. Rajeesh +2 more
- pp 557-562
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TLDR
The results indicate that the contourlet coefficient texture is effective for classifying malignant and benign liver tumors from abdominal CT imaging.Abstract:
Computed tomography image based Computer Aided Diagnosis (CAD) could be crucially important in supporting liver cancer diagnosis. An effective approach to realize a CAD system for this purpose is described in this work. The CAD system employs automatic tumor segmentation, texture feature extraction and characterization into malignant and benign tumors. A Region of Interest (ROI) cropped from the automatically segmented tumor by confidence connected region growing and alternative fuzzy c means clustering is decomposed using multiresolution and multidirectional contourlet transform to obtain contourlet coefficients. Both first order statistic and second order statistic features are extracted from the gray level and contourlet detail coefficients. The extracted feature sets are classified by a Probabilistic Neural Network (PNN) classifier into benign and malignant. The system is evaluated by using different performance measures and the results indicate that the contourlet coefficient texture is effective for classifying malignant and benign liver tumors from abdominal CT imaging.read more
Citations
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Journal ArticleDOI
Review on the methods of automatic liver segmentation from abdominal images
Suhuai Luo,Xuechen Li,Jiaming Li +2 more
TL;DR: A new way of summarizing the latest achievements in automatic liver segmentation is presented, which categorise a segmentation method according to the image feature it works on, therefore better summarising the performance of each category and leading to finding an optimal solution for a particular segmentation task.
Journal ArticleDOI
Velocity Bounded Boolean Particle Swarm Optimization for improved feature selection in liver and kidney disease diagnosis
TL;DR: Empirical results illustrate that the proposed system is superior in selecting elite features to achieve highest classification accuracy in the feature selection phase of intelligent disease diagnostic system.
Journal ArticleDOI
A Novel Method for Classifying Liver and Brain Tumors Using Convolutional Neural Networks, Discrete Wavelet Transform and Long Short-Term Memory Networks.
Hüseyin Kutlu,Engin Avci +1 more
TL;DR: Performance measurements show that the proposed CNN–DWT–LSTM method has a satisfactory accuracy rate at the liver tumor and brain tumor classifying and had higher performance than classifiers, such as K-nearest neighbors (KNN) and support vector machine (SVM).
Journal ArticleDOI
A Novel Liver Image Classification Method Using Perceptual Hash-Based Convolutional Neural Network
TL;DR: A hybrid model called fused perceptual hash-based CNN (F-PH-CNN) is proposed by using a perceptual hash function together with the CNN for differential diagnosis between benign and malignant masses using CT images.
Journal ArticleDOI
Computer-aided classification of liver lesions from CT images based on multiple ROI
Hussein Alahmer,Amr Ahmed +1 more
TL;DR: An automated Computer-Aided Classification (CAD) system to classify liver lesions into Benign or Malignant by extracting features from multiple ROIs shows an enhancement in the classification accuracy compared to the accuracy using a single ROI.
References
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Journal ArticleDOI
Textural Features for Image Classification
TL;DR: These results indicate that the easily computable textural features based on gray-tone spatial dependancies probably have a general applicability for a wide variety of image-classification applications.
Journal ArticleDOI
The contourlet transform: an efficient directional multiresolution image representation
Minh N. Do,Martin Vetterli +1 more
TL;DR: A "true" two-dimensional transform that can capture the intrinsic geometrical structure that is key in visual information is pursued and it is shown that with parabolic scaling and sufficient directional vanishing moments, contourlets achieve the optimal approximation rate for piecewise smooth functions with discontinuities along twice continuously differentiable curves.
Journal ArticleDOI
Seeded region growing
Richard Adams,Leanne Bischof +1 more
TL;DR: This correspondence presents a new algorithm for segmentation of intensity images which is robust, rapid, and free of tuning parameters, and suggests two ways in which it can be employed, namely, by using manual seed selection or by automated procedures.
Journal ArticleDOI
Alternative c-means clustering algorithms
Kuo-Lung Wu,Miin-Shen Yang +1 more
TL;DR: This AFCM algorithm has successfully been used in segmenting the magnetic resonance image of Ophthalmology to differentiate the abnormal tissues from the normal tissues and is recommended for use in cluster analysis.
Journal ArticleDOI
A computer-aided diagnostic system to characterize CT focal liver lesions: design and optimization of a neural network classifier
M. Gletsos,Stavroula Mougiakakou,George K. Matsopoulos,Konstantina S. Nikita,A. Nikita,D. Kelekis +5 more
TL;DR: A computer-aided diagnostic (CAD) system for the classification of hepatic lesions from computed tomography (CT) images is presented and shows that genetic algorithms result in lower dimension feature vectors and improved classification performance.