Image Thresholding Based on Hierarchical Clustering Analysis and Percentile Method for Tuna Image Segmentation

Abstract

Automatic classification of tuna image needs a good segmentation as a main process. Tuna image is taken with textural background and the tuna’s shadow behind the object. This paper proposed a new weighted thresholding method for tuna image segmentation which adapts hierarchical clustering analysisand percentile method. The proposed method considering all part of the image and the several part of the image. It will be used to estimate the object which the proportion has been known. To detect the edge of tuna images, 2D Gabor filter has been implemented to the image. The result image then threshold which the value has been calculated by using HCA and percentile method. The mathematical morphologies are applied into threshold image. In the experimental result, the proposed method can improve the accuracy value up to 20.04%, sensitivity value up to 29.94%, and specificity value up to 17,23% compared to HCA. The result shows that the proposed method cansegment tuna images well and more accurate than hierarchical cluster analysis method.

Automatic image slice marking propagation on segmentation of dental CBCT

“…The results of the automatic tooth segmentation research on CBCT data will be compared with the existing automatic methods, namely OTSU [24] and HCA [25]. To evaluate this proposed method, researchers used Misclassification Error (ME) and Relative Foreground Area Error (RAE).…”