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Ultrasound image is one of the modalities that is widely used to examine the abnormality of thyroid gland since it is relatively low-cost and safety. Fine needle aspiration biopsy (FNAB) is usually used by radiologists to determine the thyroid nodule whether malignant or benign. Commonly, malignancy of thyroid nodule determined based on shape feature. This research proposes a scheme for classifying...
This paper introduces automatic framework brain tumor detection, which detects and classify brain tumor in MR imaging. The proposed framework brain tumor detection is an important tool to detect the tumor and differentiate between patients that diagnosis as certain brain tumor and probable brain tumor due to its ability to measure regional changes features in the brain that reflect disease progression...
Segmentation of MR images is more important and is an essential process in resolving the human tissues, especially at the time of clinical analysis. Brain tissue is explicitly complex and it consists of three normal main tissues named White Matter (WM), Gray Matter (GM) and Cerebral Spinal Fluid (CSF) and abnormal tissues like tumor and edema. These normal and abnormal tissues can be detected using...
This paper presents a novel framework for brain tumor diagnosis and its grade classification based on higher order statistical texture features namely kurtosis and skewness along with selected morphological features. These features were extracted from segmented tumorous T2-weighted brain MR images, in order to distinguish high grade (HG) tumor from low grade (LG) tumor. Tumor classification is carried...
The brain is one of the vital organ of the body where it is the custodian of the involuntary and voluntary actions like walking, vision, memory. Now a days the most common brain disorders are Alzheimer's disease, Epilepsy (paralysis or stroke), tumors, brain tumors. Early diagnosis and proper treatment of brain tumors is required. The Computer Aided Diagnostic tools (CAD) can be used by the doctor...
An innovative and robust image segmentation approach has been proposed for magnetic resonance (MR) brain tumor extraction. We have proposed a novel technique to classify a given MR brain image as benign or malignant. In order to extract the features from given MR brain tumor image, we have first employed wavelet transform which is then followed by Laplacian Eigen maps (LE) so as to curtail the dimensions...
This paper proposes a new approach to recognize iris from distantly acquired facial images by utilizing multiple feature descriptors and classifiers. Firstly, Log-Gabor (LG), Contourlet Transform (CT), Gradient Local Auto-Correlation (GLAC) and Convolutional Neural Network (CNN) descriptors are employed on segmented normalized iris image and contextual eye image to extract features. Then, K-Nearest...
Spectral clustering is a suitable technique to deal with problems involving unlabeled clusters and having a complex structure, being kernel-based approaches the most recommended ones. This work aims at demonstrating the relationship between a widely-recommended method, so-named kernel spectral clustering (KSC) and other well-known approaches, namely normalized cut clustering and kernel k-means. Such...
Hypertension poses a serious atherosclerotic risk as it causes both macro- and micro circulation damage. Nailfold capillaroscopy is a valuable yet simple tool to assess microcirculation of blood capillaries. This technique is important in detecting early occurrences of scleroderma spectrum disorders and evaluating Raynaud's Phenomenon. Here it is used in detecting hypertension in patients. Current...
Classification of long duration speech, represented as varying length sets of feature vectors using support vector machine (SVM) requires a suitable kernel. In this paper we propose a novel segment-level pyramid match kernel (SLPMK) for the classification of varying length patterns of long duration speech represented as sets of feature vectors. This kernel is designed by partitioning the speech signal...
Fully automatic localization of lumbar vertebrae from clinical X-ray images is very challenging due to the variation of X-ray quality, scale, contrast, number of visible vertebrae, etc. To overcome these challenges, we present a novel framework, where we accelerate a scale-invariant object detection method using Support Vector Machines (SVM) trained on Histogram of Oriented Gradients (HOG) features...
Introduced in 1940, Pap smear test has proven to be an effective screening method to determine the different stages of cervical cancer. Identification and classification of Pap smear images to detect cervical cancer via manual screening is a challenging task for pathologists therefore increasing the chances of human error. In this paper, we propose an automatic method to detect and classify the grade...
This paper presents a model of Pulse-Coupled Neural Network (PCNN) for multispectral image segmentation. Its application for license plate recognition (LPR) is considered; this consists of three processing steps. First step extracts the license plate coordinates from the original image; second step is the PCNN-based segmentation method to obtain a binary image containing only the characters of the...
In this paper we present an automated blood vessel segmentation system algorithm for the retinal images under pathological conditions like Diabetic Retinopathy (DR) using matched filters and supervised classification techniques. Matched filter has been extensively used in the enhancement and segmentation of the retinal blood vessels due to the cross sectional similarity of the vessels to the Gaussian...
Automatic extraction of brain tumor regions from temporal sequence of MRI images has a great contribution for diagnostic assistance since it helps the expert to reduce the area of the region to analyze. However, it presents a challenging task for medical applications. Compared to single images, MRI images time series carry more rich information. Therefore, extracting the appropriate information among...
We propose a superpixel-based composite kernel framework for hyperspectral image (HSI) classification. Composite kernel methods can utilize both the spectral and the spatial information for the HSI classification. However, setting the optimal spatial neighborhood for different spatial structures is a non-trivial issue. In order to adaptively exploit the spatial contextual information, we utilize superpixel...
The development of robust object-oriented classification approaches suitable for medium to high spatial resolution satellite imagery provides a valid alternative to traditional pixel-based classification approaches. In the past, Support Vector Machines (SVM) have been tested and evaluated only as pixel-based image classifiers. Moving from pixel-based analysis to object-based analysis, a fuzzy classification...
Based on previous work on regional temporal mammogram registration, this study investigates the combination of image features measured from single regions (single features) and image features measured from the matched regions of temporal mammograms (temporal features) for the classification of malignant masses. Three SVM kernels, the multilayer perceptron kernel, the polynomial kernel, and the gaussian...
This paper proposes a method for segmentation of images containing both textual and graphical data. The method uses wavelet transformation to build the feature vector and a pattern recognition technique to classify areas of a document image. Values of wavelet coefficients distribution histogram of the source images sliding window serve as elements of a feature vector. For recognition of document area...
With the popularization of Internet access, institutions and parents have encountered serious problems to prevent access by employers and children to inappropriate content such as pornographic pages. The detection mechanisms try to circumvent or at least mitigate this problem by using of filters or mechanisms that enable the nudity detection in digital images. This paper proposes ANDImage, an adaptative...
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