Classification of medical image using sparse representation based on fisher discrimination

In today’s world, there is a dire need for the appropriate use of technology to diagnose and treat patients by analyzing medical data, which is usually in the form of images. This need calls for an in depth research in the field of data. Medical images form a vital component of a patient’s health record and are associated with manipulation, processing and handling of data by computersin this paper an improved method to classify medical images is discussed.This method encompasses concepts related to Sparse representation based classification has led to interesting image recognition results, while the dictionary used for sparse coding plays a key role in it. This paper presents a dictionary learning (DL) method to improve the pattern classification performance.This paper also uses image pre-processing techniques to select the best representative features to classify an image and to avoid the curse of dimensionality. Based on the Fisher discrimination criterion, a structured dictionary, whose dictionary atoms have correspondence to the class labels.These experiments were evaluated on leukemia and BRATS dataset.Experimental results showed the classification performance obtained 97.8% and 98.89%

کلیدواژه ها:

Sparse Representation ، automatic diagnosis ، medical image classification ، Fisher Discrimination