Hierarchical Mergence Approach to Cell Detection in Phase Contrast Microscopy Images

Phase contrast microscope is one of the most universally used instruments to observe long-term cell movements in different solutions. Most of classic segmentation methods consider a homogeneous patch as an object, while the recorded cell images have rich details and a lot of small inhomogeneous patches, as well as some artifacts, which can impede the applications. To tackle these challenges, this paper presents a hierarchical mergence approach (HMA) to extract homogeneous patches out and heuristically add them up. Initially, the maximum region of interest (ROI), in which only cell events exist, is drawn by using gradient information as a mask. Then, different levels of blurring based on kernel or grayscale morphological operations are applied to the whole image to produce reference images. Next, each of unconnected regions in the mask is applied with Otsu method independently according to different reference images. Consequently, the segmentation result is generated by the combination of usable patches in all informative layers. The proposed approach is more than simply a fusion of the basic segmentation methods, but a well-organized strategy that integrates these basic methods. Experiments demonstrate that the proposed method outperforms previous methods within our datasets

Evaluation Methods of Accuracy and Reproducibility for Image Segmentation Algorithms

Segmentation algorithms perform different on differernt datasets. Sometimes we want to learn which segmentation algoirithm is the best for a specific task, therefore we need to rank the performance of segmentation algorithms and determine which one is most suitable to that task. The performance of segmentation algorithms can be characterized from many aspects, such as accuracy and reproducibility. In many situations, the mean of the accuracies of individual segmentations is regarded as the accuracy of the segmentation algorithm which generated these segmentations. Sometimes a new algorithm is proposed and argued to be best based on mean accuracy of segmentations only, but the distribution of accuracies of segmentations generated by the new segmentation algorithm may not be really better than that of other exist segmentation algorithms. There are some cases where two groups of segmentations have the same mean of accuracies but have different distributions. This indicates that even if the mean accuracies of two group of segmentations are the same, the corresponding segmentations may have different accuracy performances. In addition, the reproducibility of segmentation algorithms are measured by many different metrics. But few works compared the properties of reproducibility measures basing on real segmentation data. In this thesis, we illustrate how to evaluate and compare the accuracy performances of segmentation algorithms using a distribution-based method, as well as how to use the proposed extensive method to rank multiple segmentation algorithms according to their accuracy performances. Different from the standard method, our extensive method combines the distribution information with the mean accuracy to evaluate, compare, and rank the accuracy performance of segmentation algorithms, instead of using mean accuracy alone. In addition, we used two sets of real segmentation data to demonstrate that generalized Tanimoto coefficient is a superior reproducibility measure which is insensitive to segmentation group size (number of raters), while other popular measures of reproducibility exhibit sensitivity to group size

Contributions à la segmentation d'image : phase locale et modèles statistiques

Ce document presente une synthèse de mes travaux apres these, principalement sur la problematique de la segmentation d’images

Intelligent Medical Image Segmentation Using Evolving Fuzzy Sets

Image segmentation is an important step in the image analysis process. Current image segmentation techniques, however, require that the user tune several parameters in order to obtain maximum segmentation accuracy, a computationally inefficient approach, especially when a large number of images must be processed sequentially in real time. Another major challenge, particularly with medical image analysis, is the discrepancy between objective measures for assessing and guiding the segmentation process, on the one hand, and the subjective perception of the end users (e.g., clinicians), on the other. Hence, the setting and adjustment of parameters for medical image segmentation should be performed in a manner that incorporates user feedback. Despite the substantial number of techniques proposed in recent years, accurate segmentation of digital images remains a challenging task for automated computer algorithms. Approaches based on machine learning hold particular promise in this regard because, in many applications, including medical image analysis, frequent user intervention can be assumed as a means of correcting the results, thereby generating valuable feedback for algorithmic learning. This thesis presents an investigation of the use of evolving fuzzy systems for designing a method that overcomes the problems associated with medical image segmentation. An evolving fuzzy system can be trained using a set of invariant features, along with their optimum parameters, which act as a target for the system. Evolving fuzzy systems are also capable of adjusting parameters based on online updates of their rule base. This thesis proposes three different approaches that employ an evolving fuzzy system for the continual adjustment of the parameters of any medical image segmentation technique. The first proposed approach is based on evolving fuzzy image segmentation (EFIS). EFIS can adjust the parameters of existing segmentation methods and switch between them or fuse their results. The evolving rules have been applied for breast ultrasound images, with EFIS being used to adjust the parameters of three segmentation methods: global thresholding, region growing, and statistical region merging. The results for ten independent experiments for each of the three methods show average increases in accuracy of 5\%, 12\% and 9\% respectively. A comparison of the EFIS results with those obtained using five other thresholding methods revealed improvements. On the other hand, EFIS has some weak points, such as some fixed parameters and an inefficient feature calculation process. The second approach proposed as a means of overcoming the problems with EFIS is a new version of EFIS, called self-configuring EFIS (SC-EFIS). SC-EFIS uses the available data to estimate all of the parameters that are fixed in EFIS and has a feature selection process that selects suitable features based on current data. SC-EFIS was evaluated using the same three methods as for EFIS. The results show that SC-EFIS is competitive with EFIS but provides a higher level of automation. In the third approach, SC-EFIS is used to dynamically adjust more than one parameter, for example, three parameters of the normalized cut (N-cut) segmentation technique. This method, called multi-parametric SC-EFIS (MSC-EFIS), was applied to magnetic resonance images (MRIs) of the bladder and to breast ultrasound images. The results show the ability of MSC-EFIS to adjust multiple parameters. For ten independent experiments for each of the bladder and the breast images, this approach produced average accuracies that are 8\% and 16\% higher respectively, compared with their default values. The experimental results indicate that the proposed algorithms show significant promise in enhancing image segmentation, especially for medical applications