Widyaningrum R, et al. Imaging Sci Dent. 2022 Oct;52:e42. https://doi.org/10.5624/isd.20220105

Hematological disorders of leukocytes (white blood corpuscles) are very frequent in medical practices. Each year, the number of new cases of leukemia increased with a high mortality rate what can cause a delay in diagnosis and treatment of leukemia. The hematologist faces difficulty in classifying white blood cells included cancerous leukemia cells. Currently, the diagnosis of hematological disorder was made by microscopic analysis of peripheral blood smear by hematological experts. However, this kind of diagnosis is complex, costly process, time-consuming and inherently subjective, hence to avoid such problems a classification technique based on computer vision is required. This document provides a survey of many kinds of literature which talk about leukemia detection using image processing. In this year, many improvements were made in this field. With this document, we provide a new perspective from many kinds of literature so future research can be developed effectively for their research.

Implementation of automation technology for grading tobacco leaf was very promising. In Indonesia, grading tobacco leaf was done manually and relied on the skill and experience of tobacco leaf graders. Large tobacco plantation needed many graders, and the workers needed to be trained, to become a skilled grader. It would take a long time and substantial cost to prepare sufficient graders. Even if the plantation had enough graders, monotonous and long duration of work would raise the human error. Therefore, we proposed a method for grading tobacco leaf based on color and quality using image processing techniques. This work covered quality inspection of tobacco leaf, namely leaf defect detection and classification of tobacco leaf based on color. Image processing techniques such as image thresholding, morphological operation, blob detection, and color analysis of tobacco leaf were employed to determine the grade of tobacco leaf. From the experiment, the proposed method was able to detect a leaf defect and able to classify tobacco leaf with 91.667% accuracy.

Klasifikasi Sel Darah Putih Menggunakan Metode Support Vector Machine (SVM) Berbasis Pengolahan Citra Digital

 Parrot AR.Drone is one type of quadrotor UAV. Quadrotor is operated manually with remote control and automatically using GPS (Global Positioning System), but using GPS in tracking mission an object has disadvantage that can’t  afford quadrotor position relative to object. Quadrotor require other control methods to perform object tracking. One approach is utilize digital image processing. In this research is designed detection and tracking ball system with digital image processing using OpenCV library and implemented on platform Robot Operating System. The methods which used is hough transform circle, blob detection and camshif.             The results of this research is system on AR.Drone capable of detecting and tracking ball. Based on the test results it was concluded that the maximum distance of system is capable to detecting ball with diameter of 20 cm using hough transform circle method is 500 cm and using blob detection method is 900 cm. Average time detection process to detect the ball using hough transform circle that is 0.0054 second and  for blob detection method is 0.0116 second. The success rate of tracking the ball using camshift method from the results of detection hough circle transfom is 100% while from result of detection blob detection is 96.67%

This research aims to implement method based on digital image processing to inform the status of parking slots at the car parking area by using a feature extraction HOG (Histogram of Oriented Gradients) method in every region of the parking area. Feature extraction results are classified using SVM (Support Vector Machine) by comparing the Linear, RBF (Radial Basis Function), Poly, and Sigmoid kernels. SVM classification results were analyzed using the confusion matrix with accuracy, specificity, sensitivity, and precision parameters. In terms of accuracy, system obtained with Linear kernel in sunny conditions shows 98.0% accuracy; rainy 98.8% accuracy; cloudy 99.2% accuracy. Obtained accuracy using Poly kernel test in sunny conditions shows 99.2%; rainy 98.9%; cloudy 99.4%. Obtained accuracy using RBF kernel in sunny conditions shows 97.9%; rainy 98.7%; cloudy 99.6%. In terms of accuracy using additional data testing obtained with Linear kernel shows accuracy of 97.7%; RBF kernel 97.9% accuracy;  Poly kernel 97.4% accuracy. Sigmoid kernel testing can’t be used because the optimal model did not obtained by using default grid.