Image patch-based method for automated classification and detection of focal liver lesions on CT

Abstract

We developed a method for automated classification and detection of liver lesions in CT images based on image patch representation and bag-of-visual-words (BoVW). BoVW analysis has been extensively used in the computer vision domain to analyze scenery images. In the current work we discuss how it can be used for liver lesion classification and detection. The methodology includes building a dictionary for a training set using local descriptors and representing a region in the image using a visual word histogram. Two tasks are described: a classification task, for lesion characterization, and a detection task in which a scan window moves across the image and is determined to be normal liver tissue or a lesion. Data: In the classification task 73 CT images of liver lesions were used, 25 images having cysts, 24 having metastasis and 24 having hemangiomas. A radiologist circumscribed the lesions, creating a region of interest (ROI), in each of the images. He then provided the diagnosis, which was established either by biopsy or clinical follow-up. Thus our data set comprises 73 images and 73 ROIs. In the detection task, a radiologist drew ROIs around each liver lesion and two regions of normal liver, for a total of 159 liver lesion ROIs and 146 normal liver ROIs. The radiologist also demarcated the liver boundary. Results: Classification results of more than 95% were obtained. In the detection task, F1 results obtained is 0.76. Recall is 84%, with precision of 73%. Results show the ability to detect lesions, regardless of shape.