Color Image Segmentation using Automated K-Means clustering with RGB and HSV Color Spaces

Segmentation implies the division of an image into different objects or connected regions that do not overlap Though extensive research has been done in creating many different approaches and algorithms for image segmentation however it is still not very clear to assess whether one algorithm produces more accurate segmentations than another whether it be for a particular image or set of images or more generally for a whole class of images 7 A reliable and accurate segmentation of an image is in general very difficult to achieve by purely automatic means Present researches on image segmentation using clustering algorithms reveals that K-means clustering algorithm so far produces best results but some improvements can be made to improve the results The biggest disadvantage of our heavy usage of k-means clustering is that it means we would have to think of a k each time which really doesn t make too much sense because we would like to algorithm to solve this on his own Therefore we tried to find the K automatically and so create segmentation without any human giving hints to the algorithm So we tried to make the process automatic In this paper the combined segmentation of RGB and HSV color spaces give more accurate segmentation result compared to segmentation of single color space For keeping the k parameter as small as possible we had to keep different intensity levels of the same color on the same segment to estimate the right k automatically for the algorith

Apple Fruit Grading and Disease Detection Using Classification Techniques

With extended goals for natural item consequences of choice and prosperity gages, the prerequisite for exact, snappy and target quality determination of these traits in common item things continues creating. PC vision gives one particular choice for a motorized, no damaging what's more, monetarily adroit framework to complete these essentials. This examination procedure in light of picture examination and taking care of has found a blended pack of various applications in the natural item industry. Motorized examination of Mac quality incorporates PC affirmation of good apples and imperfect apples in light of geometric or truthful parts got from apple pictures. This endeavor presents the late headways of picture taking care of and machine vision structure in an automated normal item quality estimation system. In provincial fragment the adequacy and the precise surveying technique is especially fundamental to grow the proficiency of produce. Customary fabulous characteristic items are conveyed to various countries and make a better than average pay. That is the reason the looking into methodology of the characteristic item is key to upgrade the way of natural items. Regardless, natural item looking into by individuals in provincial industry is not sufficient, requires considerable number of works and causes human slips. Objective of this paper is to underscore on late work gave insights with respect to a customized common item quality distinguishing proof structure. This endeavor shows the photo taking care of strategies for highlight extraction and course of action for natural item quality estimation structure

The application of positron emission tomography in radiotherapy treatment planning

Positron emission tomography (PET) is a molecular imaging technique that provides a direct and accurate evaluation of tissue function in vivo. PET of the glucose analogue 18F-fluoro-deoxy-glucose, is increasingly in use to aid in gross target volume delineation in radiotherapy treatment planning (RTP) where it shows reduced inter-observer variability. The aim of this thesis was to develop and investigate a new technique for delineating PET-GTV with sufficient accuracy for RTP. A new technique, volume and contrast adjusted thresholding (VCAT), has been developed to automatically determine the optimum threshold value that measures the true volume on PET images. The accuracy was investigated in spherical and irregular lesions in phantoms using both iterative and filtered back-projection reconstructions and different image noise levels. The accuracy of delineation for the irregular lesions was assessed by comparison with CT using the Dice Similarity Coefficient and Euclidean Distance Transformation. A preliminarily investigation of implementing the newly developed technique in patients was carried out. VCAT proved to determine volumes and delineate tumour boundaries on PET/CT well within the acceptable errors for radiotherapy treatment planning irrespective of lesion contrast, image noise level and reconstruction technique.EThOS - Electronic Theses Online ServiceGBUnited Kingdo