Classification of Cell Images Using MPEG-7-influenced Descriptors and Support Vector Machines in Cell Morphology

TL;DR

This paper presents a method for classifying blood cell images using MPEG-7-inspired descriptors combined with SVMs, achieving promising accuracy and suggesting further research in this area.

Contribution

It introduces an SVM-based classification approach utilizing extended MPEG-7 descriptors and textural features for blood cell identification from microscopy images.

Findings

Error rate of 10.8% in primary classification

Error rate of 3.1% in simplified classification

Ground truth accuracy of 90-95%

Abstract

Counting and classifying blood cells is an important diagnostic tool in medicine. Support Vector Machines are increasingly popular and efficient and could replace artificial neural network systems. Here a method to classify blood cells is proposed using SVM. A set of statistics on images are implemented in C++. The MPEG-7 descriptors Scalable Color Descriptor, Color Structure Descriptor, Color Layout Descriptor and Homogeneous Texture Descriptor are extended in size and combined with textural features corresponding to textural properties perceived visually by humans. From a set of images of human blood cells these statistics are collected. A SVM is implemented and trained to classify the cell images. The cell images come from a CellaVision DM-96 machine which classify cells from images from microscopy. The output images and classification of the CellaVision machine is taken as ground truth, a truth that is 90-95% correct. The problem is divided in two -- the primary and the simplified. The primary problem is to classify the same classes as the CellaVision machine. The simplified problem is to differ between the five most common types of white blood cells. An encouraging result is achieved in both cases -- error rates of 10.8% and 3.1% -- considering that the SVM is misled by the errors in ground truth. Conclusion is that further investigation of performance is worthwhile.