Medical Imaging Systems / MIS
Hassan Abbasi; zahra kavehvash
Volume 10, Issue 2 , August 2016, , Pages 161-174
Abstract
A novel computerized tomographic (CT) imaging structure based on the theory of compressed sensing (CS) is proposed. The main goal is to mitigate the CT imaging time and thus x-ray radiation dosage without compromising the image quality. In this study, we propose to use a novel dictionary in compressed ...
Read More
A novel computerized tomographic (CT) imaging structure based on the theory of compressed sensing (CS) is proposed. The main goal is to mitigate the CT imaging time and thus x-ray radiation dosage without compromising the image quality. In this study, we propose to use a novel dictionary in compressed sensing algorithm. Our dictionary is an optimal combination of Wavelet Transform (WT), Discrete Cosine Transform (DCT), and Total Variation (TV) transform. We utilize three quality assessment metrics including mean square error (MSE), peak signal to noise ratio (PSNR) and structural similarity (SSIM) indices to quantitatively evaluate the reconstructed images. The results show that the proposed method can generate high quality images with less artifacts while preserving edges when fewer number of view angles are used for reconstruction in a CT imaging system. This is in comparison with those results obtained from other reconstruction algorithms in view of the reconstructed image quality.
Biomedical Image Processing / Medical Image Processing
Hamid Abrishami Moghaddam; Alireza Sheikh Hasani; Abbas Mostafa; Masoume Giti; Parviz Abdolmaleki
Volume -1, Issue 2 , June 2005, , Pages 117-128
Abstract
This paper presents a CAD system for detection and diagnosis of microcalcification clusters in mammograms. The proposed algorithm is composed of three main stages. In the first stage, the image pixels are examined for corresponding to individual microcalcification objects. For this purpose, the wavelet ...
Read More
This paper presents a CAD system for detection and diagnosis of microcalcification clusters in mammograms. The proposed algorithm is composed of three main stages. In the first stage, the image pixels are examined for corresponding to individual microcalcification objects. For this purpose, the wavelet transform of the image is computed. Then two wavelet coefficients as well as two statistical features are used with a neural network for a primary classification of the image pixels. In the second stage, some noisy pixels extracted by the first step are eliminated. Then 18 features defined for each microcalcification are used with a nonlinear classifier for accurate detection of microcalcifications. For training of this classifier we used 16 regions from a database containing 379 microcalcifications. Finally, in the third stage five features defined for each microcalcification cluster with a neural network are used to recognize malignant microcalcification clusters. For training of this network, 22 clusters including 8 malignant and 14 benign cases were used. The performance of the algorithm was evaluated using a separate image set composed of 22 clusters including 10 malignant and 12 benign cases. Using these tests images and the threshold value of 0.45, the sensitivity of the algorithm was 100% and its specificity was 91.6%.
