The fast and accurate segmentation of lung nodule image sequences is the basis of subsequent processing and diagnostic analyses. However, previous research investigating nodule segmentation algorithms cannot entirely segment cavitary nodules, and the segmentation of juxta-vascular nodules is inaccurate and inefficient. To solve these problems, we propose a new method for the segmentation of lun...

Scale-invariant feature transform (SIFT) transforms a grayscale image into scale-invariant coordinates of local features that are invariant to image scale, rotation, and changing viewpoints. Because of its scale-invariant properties, SIFT has been successfully used for object recognition and content-based image retrieval. The biggest drawback of SIFT is that it uses only grayscale information a...

Classification of satellite images plays a vital role in remote sensing applications. Numerous algorithms have been developed and tested to classify a satellite image. The main purpose of these algorithms is to lessen the human efforts and errors in minimum time. Classification is performed on satellite images for various purposes. This paper presents a framework to classify a satellite image b...

A major problem in noisy image processing is the effective segmentation of its components. In this paper, we are proposing a K Medoid clustering algorithm for noisy image segmentation, which is able to segment all types of noisy images efficiently. As the presented clustering algorithm selects the centroids randomly hence it is less sensitive , to any type of noise as compare to other clusterin...

We highlight a partition clustering method, which proposes an experimental solution to the famous problem of automatic discovery of the number of clusters (k). The majority of partition clustering methods consider the manual valuation of k. Manual valuation of k may be interesting for specific domains of applications where the expert has an accurate idea of the number of clusters he wants, howe...

Abstract—Image segmentation is very important step of image analysis which is used to partitioned image into several homogenous regions by classifying pixels of whole image into different regions that exhibit similar characters. The result of image segmentation is a set of sections that together cover the whole image. This paper has presented a review on various image segmentations techniques l...

Clustering is the task of grouping a set of objects in such a way that objects are more similar to each other than those in the other groups. Various clustering algorithms were developed, but it ignores the spatial relationship between pixel values then noise can be added to the image and it does not provide edge detection accuracy. Fuzzy local information C-means is the best image clustering m...

Change detection algorithms play a vital role in overseeing the transformations on the earth surface. Unsupervised change detection has a indispensable role in an immense range of applications like remote sensing, motion detection, environmental monitoring, medical diagnosis, damage assessment, agricultural surveys, surveillance etc In this paper, a novel method for unsupervised change detectio...

Although many studies have been conducted to improve the clustering efficiency, most of the state-of-art schemes suffer from the lack of robustness and stability. This paper is aimed at proposing an efficient approach to elicit prior knowledge in terms of must-link and cannot-link from the estimated distribution of raw data in order to convert a blind clustering problem into a semi-supervised o...

The division of an image into meaningful structures, image segmentation, is often an essential step in image analysis, object representation, visualization, and many other image processing tasks. In this paper the multi-spectral satellite image is taken as input. Our aim is to enhance the accuracy and to remove the over segmentation..Mean-shift technique have been demonstrated to be capable of ...