This paper presents a novel content based image retrieval (CBIR) system based on Framelet Transform combined with gray level co-occurrence matrix (GLCM).The proposed method is shift invariant which captured edge information more accurately than conventional transform domain methods as well as able to handle images of arbitrary size. Current system uses texture as a visual content for feature ex...

Penyakit pada tanaman padi merupakan salah satu faktor yang menyebabkan turunnya tingkat produksi padi. tersebut adalah bacterial leaf blight, smut, brown spot dan sebagainya. Upaya identifikasi sejak dini penyakit dilakukan dengan pemanfaatan algoritma, satunya GLCM klasifikasi KNN. Identifikasi jenis menggunakan metode KNN berdasarkan eksktraksi fitur mengubah citra asli menjadi keabu-abuan (...

Ultrasound applications are used for diagnostic applications such as visualizing muscles, tendons, internal organs, to determine its size, structures, any lesions or other abnormalities. This paper concentrates the diagnosis of abnormalities in kidney Images based on retrieving past similar images from kidney Image Database. More and more amount of ultrasound digital images are being captured a...

The traditional gray level co-occurrence matrix (GLCM) is in two-dimensional form. Because hyperspectral imagery in the feature space has the characteristic of volumetric data, it has a great potential for three-dimensional texture analysis. Previous studies have successfully extended traditional 2D GLCM to a 3D form (Gray Level Co-occurrence Matrix for Volumetric Data, GLCMVD) for extracting f...

Gray level Co occurrence matrix (GLCM) texture analysis has been aggressively researched for decade for multiple applications. Co occurrence matrix retains the spatial and frequency information of the image while compresses the image into a fraction of size enabling the application of classifier engines for analysis. Haralick features are secondary features derived from GLCM. There have been co...

This paper presents preliminary results for the classiication of Pap Smear cell nuclei, using Gray Level Co-occurrence Matrix (GLCM) textural features. We outline a method of nuclear segment-ation using fast morphological gray-scale transforms. For each segmented nucleus, features derived from a modiied form of the GLCM are extracted over several angle and distance measures. Linear Discriminant...

Chi-square histogram distance is one of the distance measures that can be used to find dissimilarity between two histograms. Motivated by the fact that texture discrimination by human vision system is based on second-order statistics, we make use of histogram of gray-level co-occurrence matrix (GLCM) that is based on second-order statistics and propose a new machine vision algorithm for automat...

In this study, an efficient feature fusion based technique for the classification of colour texture images in VisTex album is presented. Gray Level Co-occurrence Matrix (GLCM) and its associated texture features contrast, correlation, energy and homogeneity are used in the proposed approach. The proposed GLCM texture features are obtained from the original colour texture as well as the first no...

In recent years, brain tumors become the leading cause of death in the world. Detection and rapid classification of this tumor are very important and may indicate the likely diagnosis and treatment strategy. In this paper, we propose deep learning techniques based on the combinations of pre-trained VGG-16 CNNs to classify three types of brain tumors (i.e., meningioma, glioma, and pituitary tumo...