Shape is an important aspects in recognizing plants. Several

Several researches in leaf identification did not include color information as features. The main reason is caused by a fact that they used green colored leaves as samples. However, for foliage plants-plants with colorful leaves, fancy patterns in their leaves, and interesting plants with unique shape-color and also texture could not be neglected. For example, Epipremnum pinnatum 'Aureum' and Epipremnum pinnatum 'Marble Queen' have similar patterns, same shape, but different colors. Combination of shape, color, texture features, and other attribute contained on the leaf is very useful in leaf identification. In this research, Polar Fourier Transform and three kinds of geometric features were used to represent shape features, color moments that consist of mean, standard deviation, skewness were used to represent color features, texture features are extracted from GLCMs, and vein features were added to improve performance of the identification system. The identification system uses Probabilistic Neural Network (PNN) as a classifier. The result shows that the system gives average accuracy of 93.0833% for 60 kinds of foliage plants.

One of difficulties in learning mathematics (counting) can be overcome by providing a relaxed and fun learning for preschoolers. Games can be used as an alternative solution. This study was conducted as a pilot project for reviewing the Kodu, Unity 3D and Construct 2 game platform for this purpose. The method used is the classification, review/evaluation, prototyping and analysis. Tech Review of these game platforms will be discussed as a result in this paper.

In this paper, an optimized fuzzy logic method for Magnetic Resonance Imaging (MRI) brain images segmentation is presented. The method is a technique based on a modified fuzzy c-means (FCM) clustering algorithm. The FCM algorithm that incorporates spatial information into the membership function is used for clustering, while a conventional FCM algorithm does not fully utilize the spatial information in the image. The advantages of the algorithm are that it is less sensitive to noise than other techniques, and it yields regions more homogeneous than those of other methods. Originality of this research is the methods applied on a normal MRI brain image and MRI brain images with tumor, and analyze the area of tumor from segmented images. The results show that the method effectively segmented MRI brain images with spatial information, and the segmented MRI normal brain image and MRI brain images with tumor can be analyzed for diagnosis purpose. In order to identify the area of abnormal mass of MRI brain images with tumor, it is resulted that the area is identified from 8.38 to 25.57 cm 2 .

This paper presents experiments and results on lung tuberculosis (TB) identification by using computer. This research's attempt is to reduce patient waiting time in obtaining X-ray diagnosis result on lung TB disease due to the mismatch the ratio of radiologist to the number of patients, especially in remote areas in Indonesia. To imitate radiologist which make visual examination on textural feature of thoracic X-ray images to make diagnosis, we exploit textural features calculated by computer to be used as descriptor in classifying images as TB or non-TB. We used statistical feature of image histograms by calculate five features: mean, standar deviation (std), skewness, kurtosis, and entropy. Features calculated where then reduced to two and one principal feature using Principal Componen Analysis (PCA) method. Finally, we used minimum distance classifier as classifier method based on two and one principal feature as descriptor. This experiment results shown that it is possible to classify TB and non-TB images based on statistical features on image histogram.