Medical image Segmentation plays a major role in MRI images processing; it's performed before the analysis and decision-making stages in several medical processes. Many investigators have developed several fuzzyc...
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ISBN:
(纸本)9781728131573
Medical image Segmentation plays a major role in MRI images processing; it's performed before the analysis and decision-making stages in several medical processes. Many investigators have developed several fuzzyc-means methods. In this work, a reliable automatic segmentation algorithm based on the spatial FcM clustering is developed to minimize the effect of noise and intensity inhomogeneities. This approach combines two properties of the spatial FcM using neighbor's statistical characteristics and pillar k-means. The proposed system has been implemented with simulink. Experimental results on brain MRI images show the improvement and increase the segmentation *** current research work is compared with some wellknown existed methods to show the effectiveness that contributes to the development of tools needed in computer aided diagnosis systems aiming to assist specialists in making diagnosis decisions.
Segmentation is one of the fundamental issues of image processing and machine vision. It plays a prominent role in a variety of image processing applications. In this paper, one of the most important applications of i...
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ISBN:
(纸本)9780819489326
Segmentation is one of the fundamental issues of image processing and machine vision. It plays a prominent role in a variety of image processing applications. In this paper, one of the most important applications of image processing in MRI segmentation of pomegranate is explored. Pomegranate is a fruit with pharmacological properties such as being anti-viral and anti-cancer. Having a high quality product in hand would be critical factor in its marketing. The internal quality of the product is comprehensively important in the sorting process. The determination of qualitative features cannot be manually made. Therefore, the segmentation of the internal structures of the fruit needs to be performed as accurately as possible in presence of noise. fuzzyc-means (FcM) algorithm is noise-sensitive and pixels with noise are classified inversely. As a solution, in this paper, the spatial FcM algorithm in pomegranate MR images' segmentation is proposed. The algorithm is performed with setting the spatial neighborhood information in FcM and modification of fuzzy membership function for each class. The segmentation algorithm results on the original and the corrupted Pomegranate MR images by Gaussian, Salt Pepper and Speckle noises show that the SFcM algorithm operates much more significantly than FcM algorithm. Also, after diverse steps of qualitative and quantitative analysis, we have concluded that the SFcM algorithm with 5x5 window size is better than the other windows.
In this paper, we have presented a new computer-aided technique for automatic detection of nucleated red blood cells (NRBcs) or normoblast cell from peripheral blood smear image. The proposed methodology initiates wit...
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In this paper, we have presented a new computer-aided technique for automatic detection of nucleated red blood cells (NRBcs) or normoblast cell from peripheral blood smear image. The proposed methodology initiates with the localization of the nucleated cells by adopting multilevel thresholding approach in smear images. A novel colour space transformation technique has been introduced to differentiate nucleated blood cells [white blood cells (WBcs) and NRBc] from red blood cells (RBcs) by enhancing the contrast between them. Subsequently, special fuzzyc-means (SFcM) clustering algorithm is applied on enhanced image to segment out the nucleated cell. Finally, nucleated RBc and WBc are discriminated by the random forest tree classifier based on first-order statistical-based features. Experimentally, we observed that the proposed technique achieved 99.42% accuracy in automatic detection of NRBc from blood smear images. Further, the technique could be used to assist the clinicians to diagnose a different anaemiccondition. Resumo In this paper, we have presented an automated, efficient NRBc detection methodology for identification of different anaemicconditions from peripheral blood smear images. To achieve this goal, we have introduced a new approach for intensifying the visual quality of nucleated cells (WBcs and NRBc) resulting in the improved discriminating property between nucleated and nonnucleated cells. SFcM technique is implemented to segment the nucleated cells from the smear images. Various intensity and shape variations (such as mean, standard deviation, area, etc.) are used to train the random forest tree algorithm for making an automated nucleated cell classification model, which achieves 99.42% NRBc detection accuracy using statistically significant features.
A novel unsupervised classification scheme called spatial fuzzy c-means clustering is proposed in this article. Based oil conventional fuzzyc-means algorithm, our scheme takes spatial homogeneity into consideration b...
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ISBN:
(纸本)0819451819
A novel unsupervised classification scheme called spatial fuzzy c-means clustering is proposed in this article. Based oil conventional fuzzyc-means algorithm, our scheme takes spatial homogeneity into consideration by introducing spatial membership and applying SMNF, thus improved robustness against noises or outliers. Preliminary experimental results are also shown to demonstrate effectiveness of our method.
A computer-Aided Diagnosis (cAD) system to assist a radiologist for diagnosing pulmonary emphysema from chest computed Tomography (cT) slices has been developed. The lung tissues are segmented from the chest cT slices...
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A computer-Aided Diagnosis (cAD) system to assist a radiologist for diagnosing pulmonary emphysema from chest computed Tomography (cT) slices has been developed. The lung tissues are segmented from the chest cT slices using spatialfuzzyc-means (SFcM) clustering algorithm and the Regions of Interest (ROIs) are extracted using pixel-based segmentation. The ROIs considered for this work are pulmonary emphysematous lesions namely, centrilobular emphysema, paraseptal emphysema and sub-pleural bullae. The extracted ROIs are then validated and labelled by an expert radiologist. From each ROI, features with respect to shape, texture and runlength are extracted. A competitive coevolution model is proposed for Feature Selection (FS). The model makes use of two bio-inspired algorithms namely, Spider Monkey Optimization (SMO) algorithm and Paddy Field Algorithm (PFA) as its building blocks. FS is performed as a wrapper approach, using the bio-inspired algorithms namely, SMO and PFA with the accuracy of the Support Vector Machine (SVM) classifier as the fitness function. Ten-fold cross validation technique is used in training the SVM classifier using the selected features. The model is tested using two datasets: Real-time emphysema dataset and cT emphysema database (cTED) dataset. The accuracy, precision, recall and specificity obtained for both the datasets are (81.95%, 93.74%), (72.92%, 90.61%), (72.92%, 90.61%), (86.46%, 95.3%), which are better compared to the performance of SMO and PFA algorithms applied individually for FS and the cAD system without performing FS.
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