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Faghihi, Reza
- Automated Brain Tumor Segmentation in MR Images Using a Hidden Markov Classifier Framework Trained by Svd-Derived Features
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Authors
Fazel Mirzaei
1,
Mohammad Reza Parishan
1,
Mohammadjavad Faridafshin
1,
Reza Faghihi
2,
Sedigheh Sina
2
Affiliations
1 Department of Medical Radiation Engineering, Shiraz University, IR
2 Radiation Research Center, Shiraz University, Shiraz, IR
1 Department of Medical Radiation Engineering, Shiraz University, IR
2 Radiation Research Center, Shiraz University, Shiraz, IR
Source
ICTACT Journal on Image and Video Processing, Vol 9, No 1 (2018), Pagination: 1844-1848Abstract
Interpreting brain MR images are becoming automated, to such extent that in some cases “all” the diagnostic procedure is done by computers. Therefore, diagnosing the patients is done by a comparably higher accuracy. Computer models that have been trained by a priori knowledge act as the decision makers. They make decisions about each new image, based on the training data fed to them previously. In case of cancerous images, the model picks that image up, and isolates the malignant tissue in the image as neatly as possible. In this paper we have developed an unsupervised learning system for automatic tumor segmentation and detection that can be applied to low contrast images.Keywords
Image Segmentation, Hidden Markov Model, Singular Value decomposition, Wavelet Analysis.
References
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- A New Approach for Contrast Enhancement in SPECT Imaging based on Gradient Approximation and Histogram Equalization (GAHE)
Abstract Views :144 |
PDF Views:0
Authors
Fazel Mirzaei
1,
Mohammadjavad Faridafshin
1,
Vahed Moharramzadeh
1,
Mohammadreza Parishan
1,
Reza Faghihi
2
Affiliations
1 Department of Medical Radiation Engineering, Shiraz University, IR
2 Radiation Research Center, Shiraz University, IR
1 Department of Medical Radiation Engineering, Shiraz University, IR
2 Radiation Research Center, Shiraz University, IR
Source
ICTACT Journal on Image and Video Processing, Vol 10, No 4 (2020), Pagination: 2186-2189Abstract
Nuclear medicine is playing a major role in medical diagnosis. But, due to limitations in both injected radionuclide to the patient’s body and low count rates of the detector, output images are of very low contrast. Several methods have been proposed to improve the contrast of medical images. In this study, a new method is presented for SPECT images. The proposed method is based on the combination of Gradient Approximation (GA) and Histogram Equalization (HE) algorithms to improve the image contrast. Poisson editing concept is deployed to allow the images to be edited and processed in the gradient domain before the reconstruction phase. GA is initially applied on the images to overcome the limitations of HE method. Using the GA concept, image gradients are manipulated first and then the images are reconstructed. These reconstructed images are fed as input for the HE block. Finally, results are presented both qualitatively and quantitatively.Keywords
SPECT, Contrast Enhancement, Histogram Equalization, Gradient Approximation.References
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- F. Mirzaei, M.R. Parishan, M. Faridafshin, R. Faghihi and S. Sina, “Automated Brain Tumor Segmentation in MR Images using A Hidden Markov Classifier Framework Trained by SVD-Derived Features”, ICTACT Journal on Image and Video Processing, Vol. 9, No. 1, pp. 1844-1848, 2018.
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