Texture-based classification of periventricular leukomalacia in preterm ultrasound images

Altered white brain matter structure in neonatal Ultrasound (US) images has prognostic implications for certain disorders. Commonly, physicians classify pathological white brain matter on a discrete categorical scale based on relevant qualitative characteristics. For certain pathologies, where subtle changes in structure have to be detected, this classification is too stringent. This is the case when characterizing affected white matter in the gliotic variant of Periventricular Leukomalacia (PVL), a brain disorder of very low birth weight preterm infants. The main objective of this study is to investigate quantitatively how texture information extracted from white matter regions in B-mode US images can guide physicians to a more accurate detection. A data set of 140 B-mode US images (70 non-pathological and 70 pathological) was investigated. Pathology was defined either by evolution to cystic PVL or by definite abnormality on acute MRI (ground truth). First, 7 different texture feature sets were extracted: First-Order statistics, Grey Level Co-occurrence matrix features, Run Length matrix features, Sum and Difference histogram features, Statistical features, Texture Energy Measure features and Gabor Filter features. Then, 3 classifiers were compared on these feature sets: a Bayesian Maximum A Posteriori (MAP) probability, a k Nearest Neighbor (kNN), and Fisher's Linear Discriminant (FLD) classifier. Finally, a combination of the classifiers as well as texture feature combinations based on a confidence measure, were incorporated into a multi-feature, multi-classifier algorithm. Using our method, we succeeded in identifying the pathological group with an accuracy of 92.5% and sensitivity and specificity scores that exceed those of existing non-texture based methods. Consequently, this method can improve both the prognostic finesse and the guidance of early postnatal management

Neighbourhood-consensus message passing and its potentials in image processing applications

In this paper, a novel algorithm for inference in Markov Random Fields (MRFs) is presented. Its goal is to find approximate maximum a posteriori estimates in a simple manner by combining neighbourhood influence of iterated conditional modes (ICM) and message passing of loopy belief propagation (LBP). We call the proposed method neighbourhood-consensus message passing because a single joint message is sent from the specified neighbourhood to the central node. The message, as a function of beliefs, represents the agreement of all nodes within the neighbourhood regarding the labels of the central node. This way we are able to overcome the disadvantages of reference algorithms, ICM and LBP. On one hand, more information is propagated in comparison with ICM, while on the other hand, the huge amount of pairwise interactions is avoided in comparison with LBP by working with neighbourhoods. The idea is related to the previously developed iterated conditional expectations algorithm. Here we revisit it and redefine it in a message passing framework in a more general form. The results on three different benchmarks demonstrate that the proposed technique can perform well both for binary and multi-label MRFs without any limitations on the model definition. Furthermore, it manifests improved performance over related techniques either in terms of quality and/or speed.In this paper, a novel algorithm for inference in Markov Random Fields (MRFs) is presented. Its goal is to find approximate maximum a posteriori estimates in a simple manner by combining neighbourhood influence of iterated conditional modes (ICM) and message passing of loopy belief propagation (LBP). We call the proposed method neighbourhood-consensus message passing because a single joint message is sent from the specified neighbourhood to the central node. The message, as a function of beliefs, represents the agreement of all nodes within the neighbourhood regarding the labels of the central node. This way we are able to overcome the disadvantages of reference algorithms, ICM and LBP. On one hand, more information is propagated in comparison with ICM, while on the other hand, the huge amount of pairwise interactions is avoided in comparison with LBP by working with neighbourhoods. The idea is related to the previously developed iterated conditional expectations algorithm. Here we revisit it and redefine it in a message passing framework in a more general form. The results on three different benchmarks demonstrate that the proposed technique can perform well both for binary and multi-label MRFs without any limitations on the model definition. Furthermore, it manifests improved performance over related techniques either in terms of quality and/or speed.P

Computational efficient segmentation of cell nuclei in 2D and 3D fluorescent micrographs

This paper proposes a new segmentation technique developed for the segmentation of cell nuclei in both 2D and 3D fluorescent micrographs. The proposed method can deal with both blurred edges as with touching nuclei. Using a dual scan line algorithm its both memory as computational efficient, making it interesting for the analysis of images coming from high throughput systems or the analysis of 3D microscopic images. Experiments show good results, i.e. recall of over 0.98.This paper proposes a new segmentation technique developed for the segmentation of cell nuclei in both 2D and 3D fluorescent micrographs. The proposed method can deal with both blurred edges as with touching nuclei. Using a dual scan line algorithm its both memory as computational efficient, making it interesting for the analysis of images coming from high throughput systems or the analysis of 3D microscopic images. Experiments show good results, i.e. recall of over 0.98.P