Improving Clustering Algorithms for Image Segmentation using Contour and Region Information

In image segmentation, clustering algorithms are very popular because they are intuitive and, some of them, easy to implement. For instance, the k-means is one of the most used in the literature, and many authors successfully compare their new proposal with the results achieved by the k-means. However, it is well known that clustering image segmentation has many problems. For instance, the number of regions of the image has to be known a priori, as well as different initial seed placement (initial clusters) could produce different segmentation results. Most of these algorithms could be slightly improved by considering the coordinates of the image as features in the clustering process (to take spatial region information into account). In this paper we propose a significant improvement of clustering algorithms for image segmentation. The method is qualitatively and quantitative evaluated over a set of synthetic and real images, and compared with classical clustering approaches. Results demonstrate the validity of this new approach

An information theoretic framework for image segmentation

In this paper, an information theoretic framework for image segmentation is presented. This approach is based on the information channel that goes from the image intensity histogram to the regions of the partitioned image. It allows us to define a new family of segmentation methods which maximize the mutual information of the channel. Firstly, a greedy top-down algorithm which partitions an image into homogeneous regions is introduced. Secondly, a histogram quantization algorithm which clusters color bins in a greedy bottom-up way is defined. Finally, the resulting regions in the partitioning algorithm can optionally be merged using the quantized histogram

Segmentation automatique de la matière grise et de la matière blanche par un algorithme génétique : une étude de validation

Résumé Objectif: l'observation des variations de volume de la matière grise (MG), de la matière blanche (MB), et du liquide céphalo-rachidien (LCR) est particulièrement utile dans l'étude de nombreux processus physiopathologiques, la mesure quantitative 'in vivo' de ces volumes présente un intérêt considérable tant en recherche qu'en pratique clinique. Cette étude présente et valide une méthode de segmentation automatique du cerveau avec mesure des volumes de MG et MB sur des images de résonance magnétique. Matériel et Méthode: nous utilisons un algorithme génétique automatique pour segmenter le cerveau en MG, MB et LCR à partir d'images tri-dimensionnelles de résonance magnétique en pondération Ti. Une étude morphométrique a été conduite sur 136 sujets hommes et femmes de 15 à 74 ans. L'algorithme a ensuite été validé par 5 approches différentes: I. Comparaison de mesures de volume sur un cerveau de cadavre par méthode automatique et par mesure de déplacement d'eau selon la méthode d'Archimède. 2. Comparaison de mesures surfaces sur des images bidimensionnelles segmentées soit par un traçage manuel soit par la méthode automatique. 3. Evaluation de la fiabilité de la segmentation par acquisitions et segmentations itératives du même cerveau. 4. Les volumes de MG, MB et LCR ont été utilisés pour une étude du vieillissement normal de la population. 5. Comparaison avec les données existantes de la littérature. Résultats: nous avons pu observer une variation de la mesure de 4.17% supplémentaire entre le volume d'un cerveau de cadavre mesuré par la méthode d'Archimède, en majeure partie due à la persistance de tissus après dissection_ La comparaison des méthodes de comptage manuel de surface avec la méthode automatique n'a pas montré de variation significative. L'épreuve du repositionnement du même sujet à diverses reprises montre une très bonne fiabilité avec une déviation standard de 0.46% pour la MG, 1.02% pour la MB et 3.59% pour le LCR, soit 0.19% pour le volume intracrânien total (VICT). L'étude morphométrique corrobore les résultats des études anatomiques et radiologiques existantes. Conclusion: la segmentation du cerveau par un algorithme génétique permet une mesure 100% automatique, fiable et rapide des volumes cérébraux in vivo chez l'individu normal.

Anal mucosectomy for haemorrhoids: should we start to speak Chinese?

AIM: Circular stapled mucosectomy is the standard therapy for the treatment of symptomatic third-degree haemorrhoids and mucosal prolapse. Recently, new staplers made in China have entered the market offering an alternative to the PPH stapling devices. The aim of this prospective randomized study was to compare the safety and efficacy of these new devices. METHODS: Fifty patients with symptomatic third-degree haemorrhoids were randomized to mucosectomy either by using stapler A (CPH32; Frankenman International Ltd, Hong Kong, China; n = 25) or stapler B (PPH03; Ethicon Endo-Surgery, Spreitenbach, Switzerland; n = 25). All procedures were performed by two experienced surgeons. After the stapler was fired by one surgeon, the other surgeon, who was blinded for stapler type, evaluated the stapler line. Postoperative outcome including pain, complications and patient satisfaction were analysed. RESULTS: Demographic and clinical features were no different between the groups. There was no significant difference regarding venous bleeding (P = 0.55), but arterial bleeding was significantly more frequent when stapler B was used (P < 0.001). This led to significantly more suture ligations (P = 0.002). However, no differences regarding operation time (P = 0.99), weight of the resected mucosa (P = 0.81) and height of the stapler line (anterior, P = 0.18; posterior, P = 0.65) were detected. Postoperative pain scores (visual analogue scale) and patient satisfaction were no different either (P = 0.91 and P = 0.78, respectively). No recurrence or incontinence occurred during follow-up. CONCLUSIONS: CPH32 required significantly fewer sutures for bleeding control along the stapler line after circular mucosectomy. However, operation time, rate of postoperative complications and patient satisfaction were similar in both groups.

Multimodal evaluation for medical image segmentation

This paper is a joint effort between five institutionsthat introduces several novel similarity measures andcombines them to carry out a multimodal segmentationevaluation. The new similarity measures proposed arebased on the location and the intensity values of themisclassified voxels as well as on the connectivity andthe boundaries of the segmented data. We showexperimentally that the combination of these measuresimprove the quality of the evaluation. The study that weshow here has been carried out using four differentsegmentation methods from four different labs applied toa MRI simulated dataset of the brain. We claim that ournew measures improve the robustness of the evaluation andprovides better understanding about the differencebetween segmentation methods.

Molecular characterisation of hepatitis B virus in the resident Chinese population in Panama City

Despite the effectiveness of current hepatitis B virus (HBV) vaccines, it is estimated that 350 million individuals suffer from chronic HBV infection and more than 50% of these affected individuals live on the Asian continent. Panama is a country with a great diversity of foreign groups; the Chinese community is a large example of this phenomenon. There is an urgent need to perform studies that evaluate the prevalence and the genetic diversity of HBV in this community. This study aimed to evaluate the prevalence of HBV and its genotypes and mutant variants in the Chinese population residing in Panama. In total, 320 subjects were enrolled in the study. Forty-two subjects (13.1%) were positive for HBsAg and HBV-DNA from 18 subjects revealed the presence of genotypes B2 and C1. Secondary mutations associated with drug resistance at positions rtV207L and rtN239T of the reverse transcriptase gene were identified. Additionally, the mutation pair A1762T/G1764A was found in three samples and the mutation G1896A was detected in an HBeAg-negative subject. In conclusion, to our knowledge, this is the first study to report high HBV prevalence rates in resident ethnic Chinese in Central America and the presence of genotypes B2 and C1 in this region.

3D Automated Lung Nodule Segmentation in HRCT

A fully-automated 3D image analysis method is proposed to segment lung nodules in HRCT. A specific gray-level mathematical morphology operator, the SMDC-connection cost, acting in the 3D space of the thorax volume is defined in order to discriminate lung nodules from other dense (vascular) structures. Applied to clinical data concerning patients with pulmonary carcinoma, the proposed method detects isolated, juxtavascular and peripheral nodules with sizes ranging from 2 to 20 mm diameter. The segmentation accuracy was objectively evaluated on real and simulated nodules. The method showed a sensitivity and a specificity ranging from 85% to 97% and from 90% to 98%, respectively.

The History of the European Union (Chinese version)

Descriu i analitza el procés d'integració europea des dels seus inicis, els anys cinquanta, fins als nostres dies. L'objectiu consisteix a oferir una visió de conjunt d'un procés que va començar modestament amb la integració dels mercats del carbó i de l'acer dels sis membres fundadors i que ha derivat en una Unió Europea de 27 estats membres i més de 500 milions d'habitants, amb polítiques comunes, una cooperació estreta en els àmbits més sensibles de la sobirania estatal i una moneda única en setze estats

Rank estimation of trajectory matrix in motion segmentation

A novel technique for estimating the rank of the trajectory matrix in the local subspace affinity (LSA) motion segmentation framework is presented. This new rank estimation is based on the relationship between the estimated rank of the trajectory matrix and the affinity matrix built with LSA. The result is an enhanced model selection technique for trajectory matrix rank estimation by which it is possible to automate LSA, without requiring any a priori knowledge, and to improve the final segmentation

Enhanced Model Selection for motion segmentation

In this paper a novel rank estimation technique for trajectories motion segmentation within the Local Subspace Affinity (LSA) framework is presented. This technique, called Enhanced Model Selection (EMS), is based on the relationship between the estimated rank of the trajectory matrix and the affinity matrix built by LSA. The results on synthetic and real data show that without any a priori knowledge, EMS automatically provides an accurate and robust rank estimation, improving the accuracy of the final motion segmentation