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International Journal of Image Mining (2 papers in press)
Microscopic Image Analysis for Herbal Plant Classification by Bhupendra Fataniya, Tanish Zaveri Abstract: An identification of herbal plants from its powder form is a challenging task. In this paper, a new method for identification and classification of herbal plants liquorice, rhubarb and dhatura using the microscopic image is proposed. This paper evaluates the effectiveness of the shape and texture based features with a different classifier for herbal plants classification. Three shape and five texture features are computed for each object. The effectiveness of the individual shape and texture based features set and their combinations are investigated using a support vector machine, K- nearest neighbour and ensemble classifier. The highest 94.9% classification accuracy was achieved by combining all shape features using the bagged tree ensemble classifier. While using a combination of texture-based features almost 99.8% classification accuracy is obtained using fine K- nearest neighbour and cubic-support vector machine classifier. Further, by combining shape and texture based features classification efficiency achieved is 99.3% with quadratic-support vector machine. From the analysis of simulation results, it is found that texture based features are more effective to classify a microscopic image of herbal plants. Keywords: Shape Feature; Texture Feature; Object Detection; herbal plant; Microscopic image.
Special Issue on: Medical Imaging
Segmentation of Retina Images to Detect Abnormalities Arising from Diabetic Retinopathy by AMRITA ROY CHOWDHURY, Sreeparna Banerjee Abstract: Segmentation of retina images to isolate and detect abnormalities is an important step before the classification can be performed. In this paper, we apply three popular unsupervised segmentation algorithms, namely, K means clustering, Fuzzy C means clustering and Otsu multilevel thresholding algorithm to extract dark and bright lesions caused by Diabetic Retinopathy, in the earlier stages of its prognosis. This segmentation process also helps in removing normal structures in the retina images like optic disc and blood vessel tree. The results of the best performing segmentation algorithm can subsequently be used in classification and thereby aid the ophthalmologists in diagnosing the disease. It is found that while Otsu segmentation performs best, K-means is a close second and outperforms Fuzzy C means clustering in terms of time complexity and is therefore the best choice. Keywords: Fuzzy C means; K-means; Otsu algorithms; retina image segmentation.