Shape and Texture Features for the Identification of Breast Cancer

Abstract: Our content based image retrieval (CBIR) system is capable of retrieving similar images from image database based on color, shape and texture.. Color, texture and

Furthermore, the comparative analysis from Table 3 clearly portrays that, the proposed method for personal recognition using finger knuckle shape oriented features and texture fea-

The highest top-15 identification rate obtained on the clean database (73%) is a combined method of nRGB histogram, local and global shape features and patch-based texture

The color, texture and shape features extracted are used to train the Back Propagation Feed Forward Neural Network to produce the desired output about the leaf

In this paper we propose a novel scheme for palmprint recognition using a shape and Texture based feature analysis obtained from Statistical Image Features.. The

Suganya, Content Based Image Retrieval System based on Semantic Information Using Color, Texture and Shape Features , Comput.. Nakagawa, Detecting prominent objects

Neural network models classification based on LAWS features yield 93.90% accuracy for normal-abnormal classification and 83.30% for benign-malignant classification.Experiment on

Objectives: To explore the potential of texture analysis based on run-length matrix features for classifying benign and malignant breast tumors in ultrasound imaging.. Methods: A