I Ketut Eddy Purnama

Malaria parasite identification on thick blood film using genetic programming

Thin blood film is used to know type and phase of the malaria parasite, but which is widely used in Indonesia is the thick blood film. Therefore we need a method that can identify parasites in thick blood film image with a high percentage of accuracy. This research aims to establish a more objective classification system and reduce the subjective factors of medical personnel in diagnosing the type of malaria parasite include its phase. It has three main stages, there are preprocessing, feature extraction, and classification. Preprocessing aims to eliminate the noise, feature extraction using red-green-blue channel color histogram, hue channel HSV histogram, and hue channel HSI histogram, classification using Genetic Programming to identify parasites and also to detect type and phase of the parasite. Experiment was conducted on 180 thick blood film images that classiffied into two classes. The classification has an average accuracy of 95.49% for non-parasites and 95.58% for parasites. Meanwhile when system is used to classified into six classes, testing result have an average accuracy of 90.25% not parasites, 82.25% vivax thropozoit, 75.83% vivax schizont, 81.75% vivax gametocytes, 90.75% falciparum thropozoit, 86.75% falciparum gametocytes. This research confirm that identifying malaria parasite in thick blood film is possible.

The extraction of acoustic features of infant cry for emotion detection based on pitch and formants

In this paper, we present the development of a system for translating the normal infant cries, which come from pain, sadness, hunger, fear and anger cry sounds, of ages from one day up to nine months old. The aim of this research is to analyse the sound of the crying infant, and to derive the reason why the infant is crying. In this experiment we used acoustic features characteristic determined by pitch and formants. The acoustic feature vectors are then clustered using K-means algorithm to determine the class or the reason of the cry. The proposed system perform well with the maximum accuracy of 90%.

Reproducibility of Standing Posture for X-Ray Radiography: A Feasibility Study of the BalancAid with Healthy Young Subjects

Unreliable spinal X-ray radiography measurement due to standing postural variability can be minimized by using positional supports. In this study, we introduce a balancing device, named BalancAid, to position the patients in a reproducible position during spinal X-ray radiography. This study aimed to investigate the performance of healthy young subjects’ standing posture on the BalancAid compared to standing on the ground mimicking the standard X-rays posture in producing a reproducible posture for the spinal X-ray radiography. A study on the posture reproducibility measurement was performed by taking photographs of 20 healthy young subjects with good balance control standing on the BalancAid and the ground repeatedly within two consecutive days. We analyzed nine posterior–anterior (PA) and three lateral (LA) angles between lines through body marks placed in the positions of T3, T7, T12, L4 of the spine to confirm any translocations and movements between the first and second day measurements. No body marks repositioning was performed to avoid any error. Lin’s CCC test on all angles comparing both standing postures demonstrated that seven out of nine angles in PA view, and two out of three angles in LA view gave better reproducibility for standing on the BalancAid compared to standing on the ground. The PA angles concordance is on average better than that of the LA angles.

Osteoarthritis Classification Using Self Organizing Map Based on Gabor Kernel and Contrast-Limited Adaptive Histogram Equalization

Localization is the first step in osteoarthritis (OA) classification. Manual classification, however, is time-consuming, tedious, and expensive. The proposed system is designed as decision support system for medical doctors to classify the severity of knee OA. A method has been proposed here to localize a joint space area for OA and then classify it in 4 steps to classify OA into KL-Grade 0, KL-Grade 1, KL-Grade 2, KL-Grade 3 and KL-Grade 4, which are preprocessing, segmentation, feature extraction, and classification. In this proposed system, right and left knee detection was performed by employing the Contrast-Limited Adaptive Histogram Equalization (CLAHE) and the template matching. The Gabor kernel, row sum graph and moment methods were used to localize the junction space area of knee. CLAHE is used for preprocessing step, i.e.to normalize the varied intensities. The segmentation process was conducted using the Gabor kernel, template matching, row sum graph and gray level center of mass method. Here GLCM (contrast, correlation, energy, and homogeinity) features were employed as training data. Overall, 50 data were evaluated for training and 258 data for testing. Experimental results showed the best performance by using gabor kernel with parameters α=8, θ=0, Ψ=[0 π/2], γ=0,8, N=4 and with number of iterations being 5000, momentum value 0.5 and α0=0.6 for the classification process. The run gave classification accuracy rate of 93.8% for KL-Grade 0, 70% for KL-Grade 1, 4% for KL-Grade 2, 10% for KL-Grade 3 and 88.9% for KL-Grade 4.

Abnormal condition detection of pancreatic Beta-cells as the cause of Diabetes Mellitus based on iris image

Diabetes occurs due to destruction of Beta-cells in the pancreatic islets of Langerhans with resulting loss of insulin production. The result of insufficient action of insulin is an increase in blood glucose concentration. The diagnosis of Diabetes must always be established by a blood glucose measurement made in an accredited laboratory. The alternative way to measure a deficiency of insulin from the Beta-cells of pancreatic islets uses iris diagnosis. Evaluating the iris is done by detecting the presence of some broken tissue in iris. However, conventional iris diagnosis is always concerned with the identification of syndromes rather than with the connection between abnormal iris tissue appearances and diseases. In this paper, we present a novel computerized iris inspection method aiming to address these problems for detecting insulin deficiency from the Beta-cells of pancreatic islets. First, quantitative features, textural measures are extracted from iris images by using popular digital image processing techniques. Then, Neighborhood based Modified Backpropagation using Adaptive Learning Parameters (ANMBP) method is employed to model the relationship between quantitative features and pancreatic abnormalities as caused of insulin deficiency. The effectiveness of this method is tested on 12 patients with Diabetes, and the diagnostic results predicted by the previously trained ANMBP classifiers are compared with the calculation of HOMA-B, obtained 83.3% accuracy in detecting pancreas disorders.

Sound Modeling of Javanese Traditional Music Instrument

A Gamelan set consists of several groups of different instruments. One of the groups is called Balungan. Gamelan is contructed manually by hand with simple tools so it is very hard to find two gamelan sets are totally identical. In this research we propose to construct gamelan models. The main target of this research is creating Gamelan Frequency Modeling. We propose two Frequency Balungan Models, the first model is using average value, and the other is using average value in the most dense area.

Automatic segmentation of malaria parasites on thick blood film using blob analysis

Malaria remains a public health problem in Indonesia. There are still many deaths caused by malaria, particularly in eastern Indonesia. There are two types of blood perform in malaria, thick blood film and thin blood film. In Indonesia, thin blood film is used more frequently than thick blood film. Malaria parasites can be found in thick blood film rapidly due to the higher volume of the blood used and sweeping process is not as much on thin blood, still a lot of leukocytes or white blood cells and platelets in the thick blood film, making it more difficult to identify the malaria parasite. Therefore we need a method can identify malaria parasites in thick blood film with a high percentage of accuracy. This study aims to build a segmentation system more objective and reduce subjective factors of medical personnel in the diagnosis of malaria parasites. This study has two main stages, preprocessing and segmentation. We use the HSV color space in the preprocessing and morphological operations and blob analysis on the segmentation stage. From the results can be known that the blob analysis was able to identify malaria parasites automatically.

Multi agent with multi behavior based on particle swarm optimization (PSO) for crowd movement in fire evacuation

A Simulation of human behavior are challenge topics of research in computer intelligent that has many benefits, such as to create a simulation of evacuation plan in a building when a disaster happens, like a fire and an earthquake. For evacuation of the building simulation of human behavior is needed to know which path will be passed through by the crowd. This animation simulation will use particle swarm algorithm optimization as the algorithm based with multiple behaviors and multiple targets. In this simulation, we add the behaviour of avoiding crashed between human and the algorithm modification is done which is a leader character is added. The leader behavior will lead the other agent to get out of the room. This agent based simulation movement will simulate movement in a room when an alarm signal is given, then the agent will get out of the room either individually or in groups. In this research we will use three scenario.We will compared the use of multiple target than single target and the use of leader follower behavior in any different number of agents.From the test result is obtained that the use of multiple target is much better result than use a single target and the behavior of the agent is depend on the movement of the crowd. Utilization of multibehavior with the leader characteristic who direct the other agent to reach target is more useful because it will reach the target more faster but the number of agents will affect the optimal number of leader needed.

WordNet Editor to Refine Indonesian Language Lexical Database

This paper describes an approach for editing Indonesian Language Lexical Database especially noun category and its relations. The purpose of this editor is to refine Indonesian Lexical Database that was developed in our previous researches. The visualization of the editor is using graph library with some modifications and additions. Furthermore, this editor will be web based so that everyone can participate to improve Indonesian Language Lexical Database. There is an administrator role that had to accept or reject any suggestion for the changes suggested by any member. We believe that this editing approach can also be used to improve WordNet developed in other languages.

A comparison of platelets classification from digitalization microscopic peripheral blood smear

Thrombocyte disease is usually caused by abnormalities, such as abnormalities based on the number and morphological deformities of platelets. Examples of platelet abnormalities include small platelets in Wiskottldrich syndrome, giant platelets in some chronic myeloproliferative diseases, Benard Soulier syndrome and Macrothrombocytopenia in gray platelet syndrome. The usual problem of automatic FBC analysis is that undetectable morphological abnormalities of platelets so the microscopic examination is required using peripheral blood smear. But microscopic examination also has some weakness such as subjective depend on medical analyst/pathologist. We propose an accurate method to classify plateles from digitalization microscopic peripheral blood smear using combination of second order statistic feature extraction and comparing several methods. The comparing methods are K-Nearest Neighbor (KNN) and Learning Vector Quantization (LVQ). In this feature extraction, we use Gray Level Co-occurrence Matrix (GLCM) to get Angular Second Moment (ASM), Invers Different Moment (IDM) and entropi values. Those values will be inserted as input in KNN classifier method to classify blood cell in peripheral blood smear. Classify of cells based on feature extraction values is divided into three classes (leukocytes, normal platelets and giant platelets). Based on the result of experiments, both of methods can classify platelets on all color channels with average accuracy are 83.67% for KNN and 74.75% for LVQ. So, The KNN classification method is better able than LVQ to classify platelets in peripheral blood smear.