This paper introduces an extension to Cartesian Genetic Programming (CGP), aimed at image classification problems. Individuals in the population consist of two layers of functions: image processing functions, and traditional mathematical functions. Information can be passed between these layers, and the final result can either be an image or a numerical value. This has been applied to image cla...

This paper describes the submission of the BMET group to the Subfigure Classification and Multi-Label Classification tasks of the ImageCLEF 2016 medical subtrack. Our method creates a new optimised feature extractor by using medical images to fine-tune a CNN that has been pre-trained on general image data. Our classification method shows promising result in both the the subfigure classification...

Image segmentation techniques have been widely used in medical image analysis. However, existing methods can not provide exact physical significance of segmented image regions because they are mainly based on basic image features such as grey level and texture without taking into account specialised medical knowledge. However, medical knowledge plays an indispensable role when doctors analyse m...

A method for automatic classification of computed tomography (CT) brain images of different head trauma types is presented in this paper. The method has three major steps: 1. The images are first segmented to find potential hemorrhage regions using ellipse fitting, background removal and wavelet decomposition technique; 2. For each region, features (such as area, major axis length, etc.) are ex...

In the conventional and relatively simple image processing techniques are most important task in the field of medical imaging. In this work to provide information about segmentation and classification methods that are very important for medical image processing. Ultrasound is unique in its ability to image patient anatomy and physiology in real time, providing an important, rapid and non-invasi...

Deep learning has been a growing trend in various fields of natural image classification as it performs state-of-the-art result on several challenging tasks. Despite its success, deep learning applied to medical image analysis has not been wholly explored. In this paper, we study on convolutional neural network (CNN) architectures applied to a Bosniak classification problem to classify Computed...

sar (synthetic aperture radar) image enhancement and segmentation is purpose of this thesis. sar image segmentation is a primary step before steps such as classification and target recognition. the main obstacle in sar image segmentation is inherent speckle noise. speckle noise is a multiplicative and highly destructive noise which results to intensity inhomogeneity. hence common segmentation m...

We describe an approach for the automatic modality classification in medical image retrieval task of the 2010 CLEF cross-language image retrieval campaign (ImageCLEF). This paper is focused on the process of feature extraction from medical images and fuses the different extracted visual features and textual feature for modality classification. To extract visual features from the images, we used...

Tumor is unwanted growth of unhealthy cell which increase intracranial pressure within skull. Medical image processing is the most challenging and innovative field specially MRI imaging modalities. The strategy presented in this paper involves preprocessing, segmentation, feature extraction, detection of tumor and its classification from MRI scanned brain images. Magnetic Resonance Imaging (MRI...

Medical disease examination is often based on images. Mining these images in order to obtain the classification knowledge for automatic image classification is a challenging task. This task belongs to the field of image mining. Image mining is usually not only comprised of mining a table of numbers it has also to do with transforming the image in the right image description. Both, the image des...