Multiple Feature Fuzzy c-means Clustering Algorithm for Segmentation of Microarray Images

Microarray technology allows the simultaneous monitoring of thousands of genes. Based on the gene expression measurements, microarray technology have proven powerful in gene expression profiling for discovering new types of diseases and for predicting the type of a disease. Gridding, segmentation and intensity extraction are the three important steps in microarray image analysis. Clustering algorithms have been used for microarray image segmentation with an advantage that they are not restricted to a particular shape and size for the spots. Instead of using single feature clustering algorithm, this paper presents multiple feature clustering algorithm with three features for each pixel such as pixel intensity, distance from the center of the spot and median of surrounding pixels. In all the traditional clustering algorithms, number of clusters and initial centroids are randomly selected and often specified by the user.  In this paper, a new algorithm based on empirical mode decomposition algorithm for the histogram of the input image will generate the number of clusters and initial centroids required for clustering.   It overcomes the shortage of random initialization in traditional clustering and achieves high computational speed by reducing the number of iterations. The experimental results show that multiple feature Fuzzy C-means has segmented the microarray image more accurately than other algorithms

A New Method of Gridding for Spot Detection in Microarray Images

A Deoxyribonucleic Acid (DNA) microarray is a collection of microscopic DNA spots attached to a solid surface, such as glass, plastic or silicon chip forming an array. The analysis of DNA microarray images allows the identification of gene expressions to draw biological conclusions for applications ranging from genetic profiling to diagnosis of cancer. The DNA microarray image analysis includes three tasks: gridding, segmentation and intensity extraction. The gridding process is usually divided into two main steps: sub-gridding and spot detection. In this paper, a fully automatic approach to detect the location of spots is proposed. Each spot is associated with a gene and contains the pixels that indicate the level of expression of that particular gene. After gridding, the image is segmented using fuzzy c-means clustering algorithm for separation of spots from the background pixels.  The result of the experiment shows that the method presented in this paper is accurate and automatic without human intervention and parameter presetting. Keywords: Microarray Image, Mathematical Morphology, Image Processin

Dimensionality reduction and hierarchical clustering in framework for hyperspectral image segmentation

The hyperspectral data contains hundreds of narrows bands representing the same scene on earth, with each pixel has a continuous reflectance spectrum. The first attempts to analysehyperspectral images were based on techniques that were developed for multispectral images by randomly selecting few spectral channels, usually less than seven. This random selection of bands degrades the performance of segmentation algorithm on hyperspectraldatain terms of accuracies. In this paper, a new framework is designed for the analysis of hyperspectral image by taking the information from all the data channels with dimensionality reduction method using subset selection and hierarchical clustering. A methodology based on subset construction is used for selecting k informative bands from d bands dataset. In this selection, similarity metrics such as Average Pixel Intensity [API], Histogram Similarity [HS], Mutual Information [MI] and Correlation Similarity [CS] are used to create k distinct subsets and from each subset, a single band is selected. The informative bands which are selected are merged into a single image using hierarchical fusion technique. After getting fused image, Hierarchical clustering algorithm is used for segmentation of image. The qualitative and quantitative analysis shows that CS similarity metric in dimensionality reduction algorithm gets high quality segmented image

Noise Removal in Microarray Images Using Variational Mode Decomposition Technique

Microarray technology allows the simultaneous monitoring of thousands of genes in parallel. Based on the gene expression measurements, microarray technology have proven powerful in gene expression profiling for discovering new types of diseases and for predicting the type of a disease. Enhancement, Gridding, Segmentation and Intensity extraction are important steps in microarray image analysis. This paper presents a noise removal method in microarray images based on Variational Mode Decomposition (VMD). VMD is a signal processing method which decomposes any input signal into discrete number of sub-signals (called Variational Mode Functions) with each mode chosen to be its band width in spectral domain. First the noisy image is processed using 2-D VMD to produce 2-D VMFs. Then Discrete Wavelet Transform (DWT) thresholding technique is applied to each VMF for denoising.  The denoised microarray image is reconstructed by the summation of VMFs.  This method is named as 2-D VMD and DWT thresholding method. The proposed method is compared with DWT thresholding and BEMD and DWT thresholding methods. The qualitative and quantitative analysis shows that 2-D VMD and DWT thresholding method produces better noise removal than other two methods

A Novel Approach for Feature Selection and Classifier Optimization Compressed Medical Retrieval Using Hybrid Cuckoo Search

Nowadays, huge data bases are required to store the Digital medical images so that they can be accessed easily on requirement. To retrieve the diagnostic images, radiologist and physicians are using Content based image retrieval (CBIR). Algorithms extract features like texture, edge, color and shape from an image in CBIR systems and these extracted features from the input and are compared for similarity with the features of images in database. In this paper, Lossless compression is used for storage and effective transmission in inadequate bandwidth. Visually lossless image compression is obtained using the Daubechies wavelet with Huffman coding. Gabor transforms are utilized to extract the shape and texture features from the images. Features are selected with Mutual Information (MI) and the proposed wrapper based Cuckoo Search (CS) technique. Extracted features are fed as input to the proposed partial Recurrent Neural Networks (RNN) for the classification. The network is optimized hybrid Particle Swarm Optimization and Cuckoo Search. It was observed that the classification accuracy acquired is satisfactor

Isolated cleft of the ala nasi: A report of seven cases

Craniofacial clefts other than cleft lip & palate are reported to be 1.4 to 4.9 per 100,000 live births. Of these, clefts of nose are usually associated with other clefts. Isolated cleft of Ala is rare, 0.7% of all clefts reported by Monasterio. In an analysis of photographic records of 3,500 consecutive patients with craniofacial clefts including cleft lip & palate registered with us between 1985- 2012 which were accessed through our data base, 13 patients with nasal clefts were identified, seven out of which had Isolated cleft of the Ala. All were treated by a rotation flap of the Ala with good results with the longest follow up of 14Yrs. The authors have emphasised the rarity of the condition and presented a simple surgical procedure for correction. In the opinion of the authors this very simple procedure which can be performed by the junior surgeon gives a good long term result in the management of cleft Ala

Perceived sources of stress amongst final year dental under graduate students in a dental teaching institution at Bangalore, India: A cross sectional study

Background: Dental schools are known to be highly demanding and stressful learning environments. Dentistry involves an acquisition of required academic, clinical and interpersonal skills during the course of learning. Practicing dentistry requires clinical skills and patient management skills, which also add to the stress perceived by the students. Identifying sources of stress represents the crucial first step towards advocating policy changes and strategies to alleviate the stressors and enhance students′ stress coping skills. The aim of this study was to identify self-reported sources of the stress among the final year [4 th year] dental undergraduate students in a Dental Teaching Institution in Bangalore, India. Materials and Methods: A 38 items, 4-point Likert Scale item modified Dental Environmental Stress (DES) questionnaire, addressing 5 stressor domains (living accommodation, interpersonal relationships, academics, clinical skills and miscellaneous) was administered to all final year undergraduate dental students of the Institution. Items and domains were considered to be perceived as "stressful", when students classified them as ′slightly′, ′moderately′ or ′severely stressful′. Descriptive and bivariate analyzes based on chi square tests were performed. Results: Out of the 38 items, 19 items were reported to be "stressful" by >70% of the students. Of these, examinations, difficulty in managing difficult cases, lack of patient co-operation, difficulty and amount of course work and completing clinical requirements were reported to be "stressful" by >85% of the students. Personal physical health, difficulty in making friends, staying with roommates, narcotic substance dependencies were least commonly reported to be "stressful". Discussion and Conclusion: The stress provoking factors among >70% of the students are quite similar to those reported by the researchers′ worldwide. Curricular changes, student support mechanisms at departmental/institutional level with appropriate policy changes need to be considered to assist the students in coping with identified stressors