Jamshid Shanbehzadeh

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This paper introduces a new feature vector for shape-based image indexing and retrieval. This feature classi5es image edges based on two factors: their orientations and correlation between neighboring edges. Hence it includes information of continuous edges and lines of images and describes major shape properties of images. This scheme is e8ective and(More)
Existing algorithms based on scale invariant feature transform (SIFT) and Harris corners such as edge-driven dual-bootstrap iterative closest point and Harris-partial intensity invariant feature descriptor (PIIFD) respectivley have been shown to be robust in registering multimodal retinal images. However, they fail to register color retinal images with(More)
medical images are more affected by intensity inhomogeneity rather than noise and outliers. This has a great impact on the efficiency of region-based image segmentation methods, because they rely on homogeneity of intensities in the regions of interest. Meanwhile, initialization and configuration of controlling parameters affect the performance of level set(More)
Identification of biological features and the segmentation is done more accurate by applying the artificial intelligence methods. Consequently these methods are so valuable in Medical Image Segmentation. The segmentation methods depend on many factors like disease type and image features. These factors result in remain the segmentation challengeable and(More)
this paper proposes a novel adaptive method to improve relevance feedback procedure in content based image retrieval. First, we transform low-level features to high-level ones by means of a multilayer neural network and these features are employed as the input of a radial basis function network for relevance feedback. This approach reduces the semantic gap(More)
Skin melanoma is the most dangerous type of skin cancer which is curable if diagnosed at the right time. Drawing distinction between melanoma and mole is a difficult task and needs detailed laboratory tests. Utilizing morphologic operators in segmenting and wavelet analysis in order to extract the features has culminated in better result in melanoma(More)
This paper presents the application of Wavelet Transform and Genetic Algorithm in a novel steganography scheme. We employ a genetic algorithm based mapping function to embed data in Discrete Wavelet Transform coefficients in 4x4 blocks on the cover image. The optimal pixel adjustment process is applied after embedding the message. We utilize the frequency(More)
This correspondence compares the computational complexity of the pair-wise nearest neighbor (PNN) and Linde-Buzo-Gray (LBG) algorithms by deriving analytical expressions for their computational times. It is shown that for a practical codebook size and training vector sequence, the LBG algorithm is indeed more computationally efficient than the PNN algorithm.