As deep convolutional networks (ConvNets) reach spectacular results on a multitude of computer vision tasks and perform almost as well as a human rater on the task of segmenting gliomas in the brain, I investigated the applicability for detecting and segmenting brain metastases. I trained networks with increasing depth to improve the detection rate and introduced a border-pair-scheme to reduce ...

We present a method for hierarchical image segmentation that defines a disaffinity graph on the image, over-segments it into watershed basins, defines a new graph on the basins, and then merges basins with a modified, size-dependent version of single linkage clustering. The quasilinear runtime of the method makes it suitable for segmenting large images. We illustrate the method on the challengi...

We present some experiments on text segmentation for German texts aimed at developing a method of segmenting historical texts. Since such texts have no (consistent) punctuation, we use a machine learning approach to label tokens with their relative positions in text segments using Conditional Random Fields. We compare the performance of this approach on the task of segmenting of text into sente...

This paper applies a new clustering approach for identifying and segmenting motion in image sequences. We estimate a matrix whose entries represent similarity probabilities between local motion estimates. We adopt a two step iterative algorithm which consists of a variant of the expectationmaximization algorithm for segmenting regions with similar motion. The proposed algorithm updates cluster ...

Image Segmentation has a vital role in the field of Medical Science especially in the area of cancer research for detecting tumors. Proper diagnosis and effective treatment is only possible if we have an efficient segmenting technique. In this paper we present a sound segmentation technique named as marker controlled watershed technique which is based on repeated morphological image dilation an...

Mammographic mass detection is an important task for early detection of breast cancer diagnosis and treatment. This is however still remains a challenging task. In this paper, we have proposed a multilevel thresholding algorithm for segmenting the tumor. This paper compares two most popular method, namely between class variance (Otsu) and entropy criterion (Kapur’s) methods for segmenting the t...

Segmentation of medical images using seeded region growing technique is increasingly becoming a popular method because of its ability to involve high-level knowledge of anatomical structures in seed selection process. Region based segmentation of medical images are widely used in varied clinical applications like visualization, bone detection, tumor detection and unsupervised image retrieval in...

The problem of correctly segmenting touching characters is an hard task to solve. In the recent years, many methods and algorithms have been proposed without achieving a comprehensive solution. In this paper, a novel method based on fuzzy logic is studied. The proposed method combines three features of touching characters that in other studies have been exploited one at a time. The strategy is ...

This paper presents a method for modeling and segmenting spatio-temporal shapes. The modeling part is based on obtaining a description of the statistical variations of spatio-temporal shape parameters by studying a representative training set of examples. A deformable model of spatio-temporal shapes is used for segmenting similar shapes in new image sequences. The deformations of the model are ...

Skeletal maturity is a reliable indicator of growth and skeletal bone age assessment (BAA) is used in the management and diagnosis of endocrine disorders. Bone age can be estimated from the left-hand wrist radiograph of the subject. The work presented in this paper proposes the development of an efficient technique for segmentation of hand-wrist radiographs and identifying the bones specially u...