![]() ![]() Results The proposed DDS was modeled under the real dental case of Hanoi Medical University, Vietnam including 87 dental images of five popular diseases, namely: root fracture, incluse teeth, decay, missing teeth, and resorption of periodontal bone. A new decision making procedure was designed to determine the final disease from a group of diseases found from the segments. A new graph-based clustering algorithm called APC+ for the classification task was proposed. It utilized the best dental image segmentation method based on semi-supervised fuzzy clustering for the segmentation task. Methods The current work proposed a novel framework called Dental Diagnosis System (DDS) for dental diagnosis based on the hybrid approach of segmentation, classification and decision making. Subclinical disease has no recognizable clinical findings, thus it is desirable to segment the dental X-Ray image into groups and then use soft computing methods to check the possibility of whether or not any disease occurs therein. To achieve better results as well as for validation, clustering is usedīackground Computerized medical diagnosis systems from X-Ray images are of great interest to physicians for accurate decision making of possible diseases and treatments. These can be preprocessed to reduce runtime processing in compression. The universal codebook generation is the key which will reduce the overhead of vector finding. The experimental results show that ELAC-VQ approach reduces the computationalĬomplexity, increases the compression percentage and speeds up the vector quantization process. At the time of decompression is simple to audio compression. A centroid-based compression reduces the operation of the comparison with the codebook and helps to improve the performance. The proposed method is Efficient Lossy AudioĬompression using vector quantization (ELAC-VQ). Simple Codebook generation algorithm is used which enhances the compression process. The important tasks in vector quantization are codebook generation and searching. ![]() Vector quantization is an effective way of lossy compression technique. Audio compression method has two types: lossy and lossless compression. In this paper, the focus is on the audio compression method. ![]() Most significantly, being multimedia compression. To achieve better results as well as for validation, clustering is used for generalized codebook generation.Ĭompression is the technique for effective utilization of space in servers as well as in personal computers. The experimental results show that ELAC-VQ approach reduces the computational complexity, increases the compression percentage and speeds up the vector quanti-zation process. The proposed method is Efficient Lossy Audio Compression using vector quantization (ELAC-VQ). The important tasks in vector quantization are code-book generation and searching. Compression is the technique for effective utilization of space in servers as well as in personal computers. ![]()
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