Towards robust partially supervised multi-structure medical image segmentation on small-scale data

Multi-instance Methods for Partially Supervised Image Segmentation

In this paper, we propose a new partially supervised multiclass image segmentation algorithm. We focus on the multi-class, singlelabel setup, where each image is assigned one of multiple classes. We formulate the problem of image segmentation as a multi-instance task on a given set of overlapping candidate segments. Using these candidate segments, we solve the multi-instance, multi-class proble...

Partially supervised clustering for image segmentation

All clustering algorithms process unlabeled data and, consequently, suffer from two problems: (P1) choosing and validating the correct number of clusters; and (P2) insuring that algorithmic labels correspond to meaningful physical labels. Clustering algorithms such as hard and fuzzy c-Means, based on optimizing sums of squared errors objective functions, suffer from a third problem: (P3) a tend...

Scale selection for supervised image segmentation

Towards Direct Medical Image Analysis without Segmentation

Direct methods have recently emerged as an effective and efficient tool in automated medical image analysis and become a trend to solve diverse challenging tasks in clinical practise. Compared to traditional methods, direct methods are of much more clinical significance by straightly targeting to the final clinical goal rather than relying on any intermediate steps. These intermediate steps, e....

Robust Potato Color Image Segmentation using Adaptive Fuzzy Inference System

Potato image segmentation is an important part of image-based potato defect detection. This paper presents a robust potato color image segmentation through a combination of a fuzzy rule based system, an image thresholding based on Genetic Algorithm (GA) optimization and morphological operators. The proposed potato color image segmentation is robust against variation of background, distance and ...