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The progress of medical imaging technologies, from X-ray radiography, ultrasonic graph to modern age's Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) scan has helped the advance of the medical technology as well as the improvement of medical care quality all over the world. It is essential to promote our own medical imaging technologies so as to reduce the future overall medical expense...
Segmentation of microcalcifications (MCs) significantly influences the performance of shape-based method for the diagnosis of MCs, which continues to be a challenge as it tends to have high false positive results. Texture based characterization of MCs represents a possible alternative that does not require prior segmentation of MCs and may improve the positive predictive value of automated diagnosis...
Accurate analysis of 2D echocardiographic images is vital for diagnosis and treatment of heart related diseases. For this task, extraction of cardiac borders must be carried out. In particular, automatic quantitative measurements of Left Ventricle (LV), Right Ventricle (RV), Left Atrium (LA), Right Atrium, Valve size, etc. are essential. We believe that automatic processing of these echo images could...
Diabetic-related eye diseases are the most common cause of blindness in the world. Early detection through regular screenings is the most effective treatment for these eye diseases. To improve the efficiency of such screenings, it is very important that effectively finding the presence of abnormalities in the retinal images captured during the screenings. In this paper, it is focused on automatically...
A novel remote-sensing image segmentation method is presented in the framework of Normalized Cuts to solve the perceptual grouping problem by means of graph partitioning. In this method, texton is applied to obtain color features and texture features of remote-sensing image. Clustering of the original color values and the filter responses of the images is performed to find texton. The filter bank...
In this paper we propose a new density based clustering algorithm. As with other density based clustering algorithms our approach does not require the number of clusters as input. A modification of the Kuwahara filter, used in image processing, is used to generate a special density map in which the brightness of pixels is indicative of the density of the data points. A framework for clustering is...
Intensity inhomogeneity or intensity non-uniformity (INU) is an undesired phenomenon that represents the main obstacle for MR image segmentation and registration methods. Various techniques have been proposed to eliminate or compensate the INU, most of which are embedded into clustering algorithms. This paper proposes a hybrid C-means clustering approach to replace the FCM algorithm found in several...
This paper presents PWEM, a technique for detecting class label noise in training data. PWEM detects mislabeled examples by assigning to each training example a probability that its label is correct. PWEM calculates this probability by clustering examples from pairs of classes together and analyzing the distribution of labels within each cluster to derive the probability of each label's correctness...
We consider here image segmentation as a problem of clustering texture features by frequency content. Specifically, we develop a low complexity algorithm for image segmentation that operates directly on the bitstream of JPEG compressed images. Using morphological filtering and watersheds, the algorithm effectively segments an image by combining areas of similar frequency content. Its low complexity...
The paper puts forward a new filtering algorithm for the restoration of digital images corrupted by impulse noise. The proposed filter includes a Hard-C means clustering stage in the impulse detection phase to facilitate pixel classification and an adaptive impulse filtering scheme in the restoration phase. The impulse detection phase avoids mis-classification of signals as impulses by identifying...
The objective of this work is the detection of object classes. An improved method is used for object detection and segmentation in real-world multiple-object scenes. It has two stages. In the first stage this method develops a novel technique to extract class-discriminative boundary fragments, and then boosting is used to select discriminative boundary fragments (weak detectors) toform a strong "boundary-fragment-model"...
A fast and efficient approach for color image segmentation is proposed. In this work, a new quantization technique for HSV color space is implemented to generate a color histogram and a gray histogram for K-Means clustering, which operates across different dimensions in HSV color space. Compared with the traditional K-Means clustering, the initialization of centroids and the number of cluster are...
This paper proposes two efficient approaches for automatic detection and extraction of Exudates and Blood vessels in ocular fundus images. The blood vessel extraction algorithm is composed of three steps, i.e., matched filtering, thresholding and label filtering. The identification of exudates involves Preprocessing, Optic disk elimination, and Segmentation of Exudates. In both the methods the enhanced...
This paper presents an effective adaptive algorithm based on cluster analysis for enlarging and demosaicking RGB images using Bayer CFA pattern images. The proposed method is efficient in fixed-point hardware implementation, and outperforms the existing weighing approach in terms of the perceptual quality. Experimental results show that this adaptive algorithm is effective both in implementation and...
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