This study aims to perform the sharpening of the dental x-ray image in the form of a panoramic dental x-ray. The method used in this study was segmentation morphology consisting of the dilation, erosion and gradient process. This study also developed a process of morphology gradient of subtracting morphology dilation results with the results of morphology erosion dilation in iterating basis. The results achieved indicate that the image enhancement process in each iteration stage can display caries objects clearly, making it easier to identify proximal caries. In this study have been compiled a looping morphology gradient algorithm which is called multiple morphology gradient.
<span>Panoramic X-Ray produces produces the most common oral digital radiographic image that it used in dentistry practice. The image can further improve accuracy compared to analog one. This study aims to establish proximal caries edge on enhancement images so they can be easily recognized. The images were obtained from the Department of Radiology, General Hospital of M. Djamil Padang Indonesia. Total file of images to be tested were 101. Firstly, the images are analyzed by dentists who practiced at Segment Padang Hospital Indonesia. They concluded that there is proximal caries in 30 molar teeth. Furthermore, the images were processed using Matlab software with the following steps, i.e. cropping, enhancement, edge detection, and edge enhancement. The accuracy rate of detection of edge enhancement images being compared with that of dentist analysis was 73.3%. In the edge enhancement images proximal caries edge can be found conclusively in 22 teeth and dubiously in eight teeth. The results of this study convinced that edge enhancement images can be recommended to assist dentists in detecting proximal caries. </span>
The study was aimed at determining the feature of a motif found in a Songket image in order to make the object detectable and readable. The method used was image color segmentation in the form of a process of segmentation of the image area based on the similarity in colors, which was continued with the binary process that aims to change the image into binary form (0 and 1), so that it only has two colors namely black and white. This study also used mathematical morphology in detecting objects, by using dilation operation and filling holes. After the process of mathematical morphology was completed, the next process was motif extraction by applying moore contour tracking algorithms and the development of chain code algorithms. The results of the process carried out showed that the development chain code algorithm can generate the number of objects, the length of chain code, and probable value of rate of appearances of each chain code in a motif, despite there are some objects in a motif. Then the values are stored into the database as The Feature of Songket Motifs.
Dental caries is tooth decay caused by bacterial infections. It is commonly known as cavities. This infection causes demineralization and hence destruction of the teeth. Diagnosis of dental caries is conventionally facilitated with radiographical films. This research aims to develop some algorithm of the mMG method in identifying dental caries based using digital panoramic dental x-ray images. This paper presents an algorithm of using digital panoramic dental x-ray images to detect dental caries. Type of algorithm used in this study is normal mMG, Enhancement mMG, and Smooth mMG. This study makes use of MATLAB and it performs dental caries detection in three algorithms. A dataset of 225 digital panoramic dental x-ray images in .png format is used to edge detection of the object in dental. The results are helpful to identify such caries from the tooth.
This study aims to facilitate the identification of proximal caries in the Panoramic Dental X-Ray image. Twenty-seven X-Ray images of proximal caries were elaborated. The images in digital form were processed using Matlab and Multiple Morphological Gradients. The process produced sharper images and clarifies the edges of the objects in the images. This makes the characteristics of the proximal caries and the caries severity can be identified precisely.
This paper proposes a method of extraction, classification and pattern recognition songket cloth texture. Features Chaincode pattern texture (Chain-code pattern texture features) are used as the basis songket search in the database or referred to as a texture-based songket pattern recognition which is part of a content-based image retrieval (CBIR). This method consists of two parts: the first is the process of establishing databases feature Chain-code pattern texture songket and training process of pattern recognition using backpropagation neural network (BPNN), the second is the retrieval process to recognize the pattern songket (songket pattern recognition and retrieval). The proposed method is a combination of several algorithms: color image segmentation, binarization, cropping, edge detection/pattern, feature extraction pattern (probability widened chain-code datasets) and BPNN training and test. Results of tests on 40 different songket motifs with training data showing the level of accuracy of the proposed method. Results of tests on 40 songket motifs show a good degree of accuracy of proposed method where the precision value reached 98% and recall value reached 99%.
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