ABSTRACT
BACKGROUND: In recent years, veritable image processing systems have been developed for several field applications, some of which are recognition and classification. One such application is in the medical field for teeth color matching systems. The color matching technique is a feasible solution for classifying patients' teeth images to evaluate the suitable treatment of tooth replacement in dentistry. However the lighting conditions of the environment and visual teeth color deficiency will be influenced or affected by the color matching performance. METHODS: This paper proposes the comparative analysis of a color matching system, using K-nearest neighbors (KNN), neural network (NN), and decision tree (DT) algorithms to classify and recognize 16 types of dental images of persons that used several extracted features, from shade guide of teeth, with a digital camera, ranging from 250-300 lux lighting value. The extracted features are produced from RGB, HSV, and Lab color moment characteristic calculation of tooth samples. Those features were compared with input images using euclidean distance value. RESULTS: KNN algorithm in RGB characteristic achieves 97.5% within only a 0.02 second computation time. CONCLUSION: KNN algorithm in RGB characteristic provides the best performance when compared to the other approaches.
ABSTRACT
In this chapter, the computational biology of cardiac cavity images is proposed. The method uses collinear and triangle equation algorithms to detect and reconstruct the boundary of the cardiac cavity. The first step involves high boost filter to enhance the high frequency component without affecting the low frequency component. Second, the morphological and thresholding operators are applied to the image to eliminate noise and convert the image into a binary image. Next, the edge detection is performed using the negative Laplacian filter and followed by region filtering. Finally, the collinear and triangle equations are used to detect and reconstruct the more precise cavity boundary. Results obtained have proved that this technique is able to perform better segmentation and detection of the boundary of cardiac cavity from echocardiographic images.
