Diagnosis of Liver Tumor from CT Images Using Fast Discrete Curvelet Transform
S. S. Kumar,R. S. Moni +1 more
TLDR
A comparison with a similar algorithm based on Wavelet texture descriptors shows that using FDCT based texture features significantly improves the classification rate of liver tumours from CT scans.Abstract:
In this paper, a novel feature extraction scheme is proposed, based on multiresolution fast discrete curvelet transform for computer-aided diagnosis of liver diseases. The liver is segmented from CT images using adaptive threshold detection and morphological processing. The suspected tumour region is extracted from the segmented liver using FCM clustering. The textural information obtained from the extracted tumour using Fast Discrete Curvelet Transform (FDCT) is used to train and classify the liver tumour into hemangioma and hepatoma employing artificial neural network classifier. A comparison with a similar algorithm based on Wavelet texture descriptors shows that using FDCT based texture features significantly improves the classification rate of liver tumours from CT scans.read more
Citations
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Journal ArticleDOI
A Survey on Artificial Intelligence Approaches for Medical Image Classification
S. N. Deepa,B. Aruna Devi +1 more
TL;DR: This study gathers representative works that exhibit how AI is applied to the solution of very different problems related to different diagnostic science analysis, and detects the methods of artificial intelligence that are used frequently together to solve the special problems of medicine.
Journal ArticleDOI
Computer-assisted frameworks for classification of liver, breast and blood neoplasias via neural networks: A survey based on medical images
TL;DR: Specific CAD frameworks are considered, where the task of feature extraction is performed by using both traditional handcrafted strategies and Convolutional Neural Networks-based innovative methodologies, whereas the final supervised pattern classification is based on neural/non-neural machine learning methods.
Proceedings Article
Automatic computer aided segmentation for liver and hepatic lesions using hybrid segmentations techniques
TL;DR: The sophisticated hybrid system was proposed in this paper which is capable to segment liver from abdominal CT and detect hepatic lesions automatically and provided good quality results, which could segment liver and extract lesions from abdominalCT in less than 0.15 s/slice.
Journal ArticleDOI
Comparison and Evaluation of Methods for Liver Tumor Classification from CT Datasets
TL;DR: An automatic system for early detection of liver diseases from Computed tomography (CT) images and textural information obtained was used to train various neural network such as Back propagation Neural Network (BPN), Probabilistic Neural network (PPN) and Cascade feed forward BPN (CFBPN).
References
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Proceedings ArticleDOI
Digital curvelet transform: strategy, implementation, and experiments
David L. Donoho,Mark R. Duncan +1 more
TL;DR: In this paper, a strategy for computing a digital curvelet transform, Curvelet 256, is described, implementing this strategy in the case of 256 X 256 images, and some experiments have been conducted using it.
Journal ArticleDOI
A Fuzzy C-Means (FCM)-Based Approach for Computerized Segmentation of Breast Lesions in Dynamic Contrast-Enhanced MR Images1
TL;DR: A fuzzy c-means (FCM) clustering-based method for the segmentation of breast lesions in three dimensions from contrast-enhanced MR images was shown to be effective and efficient.
Journal ArticleDOI
Classification algorithms for quantitative tissue characterization of diffuse liver disease from ultrasound images
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