Evaluation and Implementation of Otsu and Active Contour Segmentation in Contrast-Enhanced Cardiac CT Images

The CT cardiac acquisition process is usually conducted by using an additional image with contrast medium that is injected inside the body and reconstructed by a radiologist using an integrated CT Scan software with the aim to find the morphology and volume dimension of the heart and coronary arteries. In fact, the data obtained from the hospital are raw data without segmented contour from a radiologist. For the purpose of automation, dataset is needed to be used as input data for further program development. This study is focused on the evaluation of the segmentation results of CT cardiac images using Otsu threshold and active contour algorithm with the aim to make a dataset for the heart volume quantification that can be used interactively as an alternative to integrated CT scan software. 2D contrast enhanced cardiac CT from 6 patients using image processing techniques was run on Matlab software. Of the 689 slices that was used, as many as (73.75 ± 19.41)%of CT cardiac slices have been segmented properly, (19.15 ± 19.61)%of the slices that were segmented included the spine bone, (1.36 ± 0.98)%of the slices did not include all region of the heart, (16.58 ± 15.26)%of the slices included other organs with the consistency from the measurement proven from inter-observer variability to produce r = 0,9941.The result is due to the geometry influence from the diameter of the patient’s body thickness that tends to be thin

Computer-Aided Detection (CAD) Deteksi Nodul Paru-Paru dari Computed Tomography (CT)

Nodul paru merupakan pertumbuhan jaringan abnormal pada paru yang digunakan sebagai diagnosis dini kanker paru. Kanker paru-paru adalah kanker yang paling banyak ditemukan dan mematikan di dunia. Umumnya, deteksi pertama nodul paru diperoleh dari citra CT yang didiagnosis secara visual oleh ahli radiologi. Artinya subjektivitas individu radiologis berpengaruh dalam citra diagnosis tersebut. Untuk membantu ahli radiologi dalam mendeteksi dan mengevaluasi nodul paru pada citra CT secara otomatis, penelitian ini telah mengembangkan sistem Computer-Aided Detection (CAD). Sistem CAD menggunakan metode segmentasi Otsu, dengan ekstraksi fitur Gray Level Co-occurrence Matrix (GLCM) sebagai input untuk klasifikasi nodul. Algoritma Random Forest digunakan untuk membedakan antara normal dan abnormal pada citra CT, khususnya citra dengan kelainan nodul paru. Evaluasi estimasi keberadaan nodul paru pada sistem dilakukan menggunakan Receiver Operating Characteristic (ROC) dengan sensitivitas 95%.Kata Kunci: CAD, CT dada, Deteksi nodul paru, Random Fores

Comparison of deep learning models for building two-dimensional non-transit EPID Dosimetry on Varian Halcyon

Background: This study compared the effectiveness of five deep learning models in constructing non-transit dosimetry with an a-Si electronic portal imaging device (EPID) on Varian Halcyon. Deep learning model is increasingly used to support prediction and decision-making in several fields including oncology and radiotherapy. Materials and methods: Forty-seven unique plans of data obtained from breast cancer patients were calculated using Eclipse treatment planning system (TPS) and extracted from DICOM format as the ground truth. Varian Halcyon was then used to irradiate the a-Si 1200 EPID detector without an attenuator. The EPID and TPS images were augmented and divided randomly into two groups of equal sizes to distinguish the validation and training–test data. Five different deep learning models were then created and validated using a gamma index of 3%/3 mm. Results: Four models successfully improved the similarity of the EPID images and the TPS-generated planned dose images. Meanwhile, the mismatch of the constituent components and number of parameters could cause the models to produce wrong results. The average gamma pass rates were 90.07 ± 4.96% for A-model, 77.42 ± 7.18% for B-model, 79.60 ± 6.56% for C-model, 80.21 ± 5.88% for D-model, and 80.47 ± 5.98% for E-model. Conclusion: The deep learning model is proven to run fast and can increase the similarity of EPID images with TPS images to build non-transit dosimetry. However, more cases are needed to validate this model before being used in clinical activities

A LabVIEW Based Optimization and Integration of Supersonic Wind Tunnel Instrumentation System

Indonesian National Institute of Aeronautics and Space have a supersonic wind tunnel for research in high speed object . The condition of LAPAN's supersonic wind tunnel can only be used for shockwave observation by using schlieren apparatus. The data acquisition system can not collect data from sting balance, some of the control panels are either not operational or it need calibration. Based from these conditions, this research is done to develop a new integrated control system and data acquisition so that the effectiveness of operation in terms of time and better data quality can be achieved. For angle of attack (AoA) control from manual operation, have been optimized to a digital control using PID control method. With the new system, the AoA control has been automated and a new testing option for moving the AoA while the wind tunnel running can be done. In terms of data acquisition, after the optimization it can collect better data, (noise / interference becomes smaller), and now it can record data from the balance, the pressure data, AoA position and block position can be recorded. The system was created using PXIe from National Instrument and LabVIEW graphical programming as user interface

Computer Aided Diagnosis (CAD) for mammography with Markov Random Field method with Simulated Annealing optimization

Mammography is the most effective technique to detect breast abnormalities. In most cases, mammograms are evaluated by radiologists. However, diagnosis performed radiologist has a lot of limitations. Computer Aided Diagnosis (CAD) with various methods had been developed to help radiologist in evaluating mammograms. This research developed CAD for mammography based on image segmentation using Markov Random Field with Simulated Annealing optimization (MRF/SA). We combined MRF/SA method with various preprocessing algorithms, such as median filter, histogram equalization, and CLAHE (Contrast Limited Adaptive Histogram Equalization). MRF/SA without any filter and contrast enhancement was also performed. A total of 210 mammograms with normal and abnormal findings were used. Abnormal category means mammogram with abnormalities findings whether in a form of benign lesion, malignant lesion, benign microcalcification or malignant microcalcification. ROC (Receiver Operating Characteristic) analysis was used to measure methods’ performance. The values of area under the ROC curve (AUC) for MRF/SA only, median filter + MRF/SA, histogram equalization + MRF/SA and CLAHE + MRF/SA are 0.731, 0.840, 0.798, and 0.746 respectively. Combination of median filter + MRF/SA has the highest AUC value indicated that this method has the best performance in distinguishing normal and abnormal images. Histogram equalization + MRF/SA has inferior AUC value compare to median filter + MRF/SA, but this combination has the highest sensitivity, 90.4%. This result shows that histogram equalization + MRF/SA is the most successful method in detecting abnormal images correctly