Automatic Feature Set Selection for Merging Image Segmentation Results Using Fuzzy Clustering

The image segmentation performance of clustering algorithms is highly dependent on the features used and the type of objects contained in the image, which limits the generalization ability of such algorithms. As a consequence, a fuzzy image segmentation using suppressed fuzzy c-means clustering (FSSC) algorithm was proposed that merged the initially segmented regions produced by a fuzzy clustering algorithm, using two different feature sets each comprising two features from pixel location, pixel intensity and a combination of both, which considered objects with similar surface variations (SSV), the arbitrariness of fuzzy c-means (FCM) algorithm using pixel location and the connectedness property of objects. The feature set selection for the initial segmentation in the merging technique was however, inaccurate because it did not consider all possible feature set combinations and also manually defined the threshold used to identify objects having SSV. To overcome these limitations, a new automatic feature set selection for merging image segmentation results using fuzzy clustering (AFMSF) algorithm is proposed, which considers the best feature set selection and also calculates the threshold based upon human visual perception. Both qualitative and quantitative analysis prove the superiority of AFMSF algorithm compared with other clustering techniques including FSSC, FCM, possibilistic c-means (PCM) and SFCM, for different image types

Fuzzy Clustering for Image Segmentation Using Generic Shape Information

The performance of clustering algorithms for image segmentation are highly sensitive to the features used and types of objects in the image, which ultimately limits their generalization capability. This provides strong motivation to investigate integrating shape information into the clustering framework to improve the generality of these algorithms. Existing shape-based clustering techniques mainly focus on circular and elliptical clusters and so are unable to segment arbitrarily-shaped objects. To address this limitation, this paper presents a new shape-based algorithm called fuzzy clustering for image segmentation using generic shape information (FCGS), which exploits the B-spline representation of an object's shape in combination with the Gustafson-Kessel clustering algorithm. Qualitative and quantitative results for FCGS confirm its superior segmentation performance consistently compared to well-established shape-based clustering techniques, for a wide range of test images comprising various regular and arbitrary-shaped objects

Image segmentation using fuzzy clustering incorporating spatial information

Effective image segmentation cannot be achieved for a fuzzy clustering algorithm based on using only pixel intensity, pixel locations or a combination of the two. Often if both pixel intensity and pixel location are combined, one feature tends to minimize the effect of other, thus degrading the resulting segmentation. This paper directly addresses this problem by introducing a new algorithm called image segmentation using fuzzy clustering incorporating spatial information (FCSI), which merges the segmented results independently generated by fuzzy clustering-based on pixel intensity and the location of pixels. Qualitative results show the superiority of the FCSI algorithm compared with the fuzzy c-means (FCM) algorithm for all three alternatives, clustering using only pixel intensity, pixel locations and a combination of the two

Fuzzy Image Segmentation using Suppressed Fuzzy C-Means Clustering

Clustering algorithms are highly dependent on the features used and the type of the objects in a particular image. By considering object similar surface variations (SSV) as well as the arbitrariness of the fuzzy c-means (FCM) algorithm for pixellocation, a fuzzy image segmentation considering object surface similarity (FSOS) algorithm was developed, but it was unable to segment objects having SSV satisfactorily. To improve the effectiveness of FSOS in segmenting objects with SSV, thispaper introduces a new fuzzy image segmentation using suppressed fuzzy c-means clustering (FSSC) algorithm, which directly considers object SSV and incorporates the use of suppressed-FCM (SFCM) using pixel location. The algorithmalso perceptually selects the threshold within the range of human visual perception. Both qualitative and quantitative resultsconfirm the improved segmentation performance of FSSC compared with other algorithms including FSOS, FCM,possibilistic c-means (PCM) and SFCM for many different images

Fuzzy image segmentation using location and intensity information

The segmentation results of any clustering algorithm are very sensitive to the features used in the similarity measure and the object types, which reduce the generalization capability of the algorithm. The previously developed algorithm called image segmentation using fuzzy clustering incorporating spatial information (FCSI) merged the independently segmented results generated by fuzzy clustering-based on pixel intensity and pixel location. The main disadvantages of this algorithm are that a perceptually selected threshold does not consider any semantic information and also produces unpredictable segmentation results for objects (regions) covering the entire image. This paper directly addresses these issues by introducing a new algorithm called fuzzy image segmentation using location and intensity (FSLI) by modifying the original FCSI algorithm. It considers the topological feature namely, connectivity and the similarity based on pixel intensity and surface variation. Qualitative and quantitative results confirm the considerable improvements achieved using the FSLI algorithm compared with FCSI and the fuzzy c-means (FCM) algorithm for all three alternatives, namely clustering using only pixel intensity, pixel location and a combination of the two, for a range of sample of images

Present status of pen and cage culture of finfishes in Southeast Asia

Cage and pen culture practices in certain Southeast Asian countries viz. India, Bangladesh, Sri Lanka, Indonesia, Singapore, Malaysia, Thailand, Cambodia, Vietnam and Philippines have been reviewed in this paper The various methods adnpted and materials used in the design and construction of cages and pens and the different species of fishes cultured in these two systems are discussed. The possibilities for adopting pen and cage culture practices for fish production in !ndia are also indicate

A new species of Argulus (Brachiura) from a marine fish Psammoperca waigiensis (Cuvier)

A specimen of ArguluJ taken from the body surface of the marine perch Psammo- perca waigiensis (Cuvier) caught from the Palk Bay near Mandapam has been found to be a new species, and its description is given here. Species and subspecies of the genus Argulus Milller so far recorded from India are A. indieus Weber, A. gigan- teus Ramakrishna, A. bengalen.ri.r Ramakrishna, A. siameni-is Wilson, A. siamensis penin.rulari.r Ramakrishna and A. puthenvelien.ri.r Ramakrishna (see Ramakrishna, 1951, 1962 ) . The postembryonic development of A. puthenvelien.ri.r has been dealt with by Thomas (1961). Thomas & Devaraj (in press) have described two new species, namely A. fluviatili.r and A. cauveriensis collected from the river Cauvery

Metastasis from tongue squamous cell carcinoma to the kidney

Metastasis to the kidney from other primary sites is extremely rare. Previous studies reported the lung as the most common primary site. Distant metastasis from the tongue to the kidney is exceedingly rare. Herein, we describe a case of metastatic squamous cell carcinoma to the kidney in a 71-year-old male with a detailed discussion of differentiating it from potential mimickers. The patient underwent a total glossectomy and bilateral cervical lymph node dissection. A diagnosis of well-differentiated squamous cell carcinoma of the tongue was rendered and the tumor was staged pT3 pN3b. Within two years of initial presentation, the patient developed widely metastatic disease, including pulmonary nodules, renal masses, left adrenal mass, and pancreatic mass. Accurate diagnosis of a secondary involvement of the kidney by a metastatic tumor requires the appropriate correlation of clinical and imaging findings as well as morphologic and immunohistochemical clues

Sampling by Divergence Minimization

We introduce a Markov Chain Monte Carlo (MCMC) method that is designed to sample from target distributions with irregular geometry using an adaptive scheme. In cases where targets exhibit non-Gaussian behaviour, we propose that adaption should be regional rather than global. Our algorithm minimizes the information projection component of the Kullback-Leibler (KL) divergence between the proposal and target distributions to encourage proposals that are distributed similarly to the regional geometry of the target. Unlike traditional adaptive MCMC, this procedure rapidly adapts to the geometry of the target's current position as it explores the surrounding space without the need for many preexisting samples. The divergence minimization algorithms are tested on target distributions with irregularly shaped modes and we provide results demonstrating the effectiveness of our methods.Comment: 33 pages, 12 figure