Automated classification of breast cancer morphology in histopathological images

Background The morphology of a breast cancer tumour, as examined through an optical microscope, is currently assessed visually by the pathologist in parallel with making the cancer diagnosis. The grade of differentiation, which describes how closely the morphology of the tumour resembles the corresponding healthy tissue of an organ, is undisputedly related to the outcome of breast cancer [1]. However, tumour grade is largely regarded as an unreliable prognostic factor due to its poor reproducibility [2]. The visually determined morphology is afflicted with a poor interand intra observer agreement, which prevents grade from being fully utilized as an important outcome predictor. The same pathologist may assign different grade to the same tumour when assessment is repeated, and different pathologists disagree to a substantial level when assessing the same tumour [3]. Computational diagnostic tools for estimating the morphological properties of cancer tissue would enable objective and reproducible alternative for diagnosis. This could be achieved by fully utilizing the recent advances in digital microscopy and computer vision [4,5]. Some attempts have already been made for automated grading of histopathological breast cancer images, but these studies have covered only limited amount of data or produce just a partial grading [6,7]. We propose a texture based algorithm for automated classification of breast cancer morphology. The method uses the recently introduced LPQ [8] as well as LBP [9] descriptors and an SVM classifier. The LPQ and LBP descriptors each form a histogram representing the statistical texture properties and have been used earlier in many texture analysis applications which include surface inspection [9], tissue analysis [5], and face recognition [8], whereas SVM represents the state of the art among supervised learning based classification algorithms.