MIRACLE at ImageCLEFannot 2008: Nearest Neighbour Classification of Image Feature Vectors for Medical Image Annotation

Abstract

Abstract. This paper describes the participation of MIRACLE research consortium at the ImageCLEF Medical Image Annotation task of ImageCLEF 2008. During the last year, our own image analysis system was developed, based on MATLAB. This system extracts a variety of global and local features including histogram, image statistics, Gabor features, fractal dimension, DCT and DWT coefficients, Tamura features and co-occurrence matrix statistics. A classifier based on the k-Nearest Neighbour algorithm is trained on the extracted image feature vectors to determine the IRMA code associated to a given image. The focus of our participation was mainly to test and evaluate this system in-depth and to compare among diverse configuration parameters such as number of images for the relevance feedback to use in the classification module.

Based on an improved pre-PCA + LDA classifier design in tumor cells

“…7 % , the correct identification rate for hyperplasia type is (28 -7) /2 8 = 7 5 % , The overall correct identification rate is 13 4 /163 :::; 8 2.2% , the results shown in TABLE I. In order to determine the improved PCA + LDA a certain extent has a stronger generalization ability than the traditional PCA + LDA, the characteristics of dimensionality reduction is more beneficial to post-classification, we designed a number of comparative experiments, including the nearest neighbor (KNN) classification [3] (4) Compared wIth the tradItional PCA + LDA classIfication, the classification accuracy rate of the improved PCA + LDA in classification has increased,which increases the recognition rate of cancer 8 5 % -8 2 .22% = 2. 78 % , the recognition rate of the proliferation of class increases 7 5 % -7 1 .43% = 3.…”