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This paper presents an automated system for recognizing the medicinal plant leaves that are taken from the suburbs of the western ghats region. The dataset comprises of 250 different leaf images, of five species. Texture analyses of the leaf images have been done in this work using the feature computation. The features include grey textures, grey tone spatial dependency matrices(GTSDM) and Local Binary Pattern(LBP) operators. For each leaf image, a feature vector is generated from the statistical values. 70% of the images in the dataset are the training dataset and the rest are included in the test set. Six different classifiers are used to classify the plant leaves based on feature values. When features are combined without any preprocessing, it yielded a classification performance of 94.7%. General Terms:image processing, pattern recognition

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Tuberculosis (TB) is one of the infectious diseases spread by the infectious agent Mycobacterium tuberculosis. Sputum smear microscopy is the primary tool used for the diagnosis of pulmonary TB, but has its limitations such as low sensitivity and large observation time. Hence, an automated technique is preferred for the diagnosis of TB. This paper develops a technique for TB diagnosis based on the bacilli count by proposing Fuzzy and Hyco-entropy-based Decision Tree (FHDT) classifier using sputum smear microscopic images. The proposed technique involves three steps: segmentation, feature extraction and classification. Initially, the input sputum smear microscopic image is subjected to a colour space transformation, for which a thresholding is applied to obtain the segmented result. Important features such as length, density, area and few histogram features are extracted for FHDT-based classification that classifies the segments into few-bacilli, non-bacilli and overlapping bacilli. An entropy function, called hyco-entropy, is designed for the optimal selection of feature. For further analysis of classification, that is, to count the number in the overlapping bacilli, the fuzzy classifier is adopted. FHDT classifier is evaluated in terms of Segmentation Accuracy (SA), Mean Squared Error (MSE) and Missing Count (MC) using microscopic images taken from ZNSM-iDB, where it can attain maximum mean SA of 0.954 and mean MC of 2.4.