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Measurement Straight Leg Raise for Low Back Pain Based Grayscale Depth Tavipia Rumambi; Hustinawaty Hustinawaty; Sarifuddin Madenda; Eri Prasetyo Wibowo
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 15, No 1: March 2017
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v15i1.4291

Spinal disorders are the most frequent cause of pain and lower part of the spine, which is often called Low Back Pain.Straight Leg Raise Test can provide important information to detect the cause of LBP. Straight Leg Raise test conducted by physican with a goniometer required accurately reading angle when your feet up. But this can be overcome with Kinect can detect motion and displays images and depth data. Methodological includes image acquisition method using RGB and Grayscale depth, skeleton tracking, feature extraction detection Straight Leg Raise . The proposed algorithm describes a method for estimating the data triangulation angle Straight Leg Raise by Kinect. Results measurement if the positive Low Back Pain below 60 degrees there is a tendency to suffer from one of the causes of Low Back Pain. The results can be stored in the database as medical history and used to monitor the progress of therapy

Hybrids Otsu method, Feature region and Mathematical Morphology for Calculating Volume Hemorrhage Brain on CT-Scan Image and 3D Reconstruction Sumijan Sumijan; Sarifuddin Madenda; Johan Harlan; Eri Prasetya Wibowo
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 15, No 1: March 2017
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v15i1.3146

Traumatic brain injury is a pathological process of brain tissue that is not degenerative or congenital, but rather due to external mechanical force, which causes physical disorders, cognitive function, and psychosocial. These disorders can be permanent or temporary and accompanied by the loss of or change in level of consciousness. Segmentation techniques for Computed Tomography Scanner (CT scan) of the brain is one of the methods used by the radiologist to detect abnormalities or brain hemorrhage that occurs in the brain. This paper discusses the extraction area of a brain hemorrhage on each image slice CT scan and 3D reconstruction, making it possible to visualize the 3D shape and calculating the volume of a brain hemorrhage. Extraction of brain hemorrhage area is based on a combination of Otsu algorithm, the algorithm Morphological features and algorithms region. For the reconstruction of a 3D brain hemorrhage area of the bleeding area on a 2D slice is done by using a linear interpolation approach.

Image Processing of Panoramic Dental X-Ray for Identifying Proximal Caries Jufriadif Na'am; Johan Harlan; Sarifuddin Madenda; Eri Prasetyo Wibowo
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 15, No 2: June 2017
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v15i2.6856

This study aims to facilitate the identification of proximal caries in the Panoramic Dental X-Ray image. Twenty-seven X-Ray images of proximal caries were elaborated. The images in digital form were processed using Matlab and Multiple Morphological Gradient. The process produced sharper images and clarifies the edges of the objects in the images. This makes the characteristics of the proximal caries and the caries severity can be identified precisely.

Efficient Implementation of Mean, Variance and Skewness Statistic Formula for Image Processing Using FPGA Device Aqwam Rosadi Kardian; Sunny Arief Sudiro; Sarifuddin Madenda
Bulletin of Electrical Engineering and Informatics Vol 7, No 3: September 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v7i3.687

Processing statistic formula in image processing and accessing data from memory is easy in software, the other hand for hardware implementation is more dificult considering a lot of constraint. This article proposes an implementation of optimum mean, variance and skewness formula in FGPA Device. The proposed circuit design for all formulas only need three additions component (in three accumulators) and two divisions using two shift-right-registers, two subtractors, one adder and six multipliers. For 8x8 image size need 64 clock cycles to finish the mean, variance and skewness calculations, comparing other approach that need more than 1024 additions component without skewness calculation. Implementation into FPGA needs 68 slices of flip-flops and 121 of 4 input LUTs.

Efficient Implementation of Mean, Variance and Skewness Statistic Formula for Image Processing Using FPGA Device Aqwam Rosadi Kardian; Sunny Arief Sudiro; Sarifuddin Madenda
Bulletin of Electrical Engineering and Informatics Vol 7, No 3: September 2018
Publisher : Institute of Advanced Engineering and Science

Processing statistic formula in image processing and accessing data from memory is easy in software, the other hand for hardware implementation is more dificult considering a lot of constraint. This article proposes an implementation of optimum mean, variance and skewness formula in FGPA Device. The proposed circuit design for all formulas only need three additions component (in three accumulators) and two divisions using two shift-right-registers, two subtractors, one adder and six multipliers. For 8x8 image size need 64 clock cycles to finish the mean, variance and skewness calculations, comparing other approach that need more than 1024 additions component without skewness calculation. Implementation into FPGA needs 68 slices of flip-flops and 121 of 4 input LUTs.

Ekstraksi Fitur Bentuk Tumor Payudara Aviarini Indrati; Sarifuddin Madenda
Seminar Nasional Aplikasi Teknologi Informasi (SNATI) 2009
Publisher : Jurusan Teknik Informatika, Fakultas Teknologi Industri, Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar

Kanker payudara adalah penyakit penyebab kematian wanita kedua di dunia. Citra mamografi merupakan citrayang dapat digunakan sebagai alat bantu mendeteksi keberadaan penyakit tersebut. Keberadaan penyakittersebut ditunjukkan dalam karakteristik objek tumor payudara yang tampak pada citra mamografi. Oleh karenaitulah maka pada paper ini akan dikemukakan algoritma untuk mengekstraksi fitur bentuk tumor payudara yangtampak pada citra mamografi. Algoritma disusun tahap demi tahap diawali dengan memisahkan ataumelokalisasi area yang dicurigai terdapat tumor payudara sehingga diperoleh Region of Interest (ROI),kemudian dilanjutkan dengan mendeteksi tepi objek (edge detection) tumor payudara dan penipisan tepi objek(contour delimitation) tumor payudara.Kata Kunci: ekstraksi, mamografi, region of interest (roi), edge detection, contour delimitation

Efficient Implementation of Mean, Variance and Skewness Statistic Formula for Image Processing Using FPGA Device Aqwam Rosadi Kardian; Sunny Arief Sudiro; Sarifuddin Madenda
Bulletin of Electrical Engineering and Informatics Vol 7, No 3: September 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v7i3.687

Processing statistic formula in image processing and accessing data from memory is easy in software, the other hand for hardware implementation is more dificult considering a lot of constraint. This article proposes an implementation of optimum mean, variance and skewness formula in FGPA Device. The proposed circuit design for all formulas only need three additions component (in three accumulators) and two divisions using two shift-right-registers, two subtractors, one adder and six multipliers. For 8x8 image size need 64 clock cycles to finish the mean, variance and skewness calculations, comparing other approach that need more than 1024 additions component without skewness calculation. Implementation into FPGA needs 68 slices of flip-flops and 121 of 4 input LUTs.

Three-Dimensional (3D) Reconstruction for Detecting Shape and Volume of Lung Cancer Nodules Rodiah Rodiah; Sarifuddin Madenda; Fitrianingsih Fitrianingsih
IPTEK Journal of Proceedings Series Vol 1, No 1 (2014): International Seminar on Applied Technology, Science, and Arts (APTECS) 2013
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j23546026.y2014i1.198

The development of CT scanning technology as a digital image recorder has provided facilities for oncologists in analyzing the presence of cancer in patient's organs. Visually, oncologists analyze it by looking at the CT slices to ascertain whether any cancer nodules in the lung are present. The center line of nodules is used to calculate the volume of nodules for all slices. Volume is used to monitor the rate of cancer growth. Another way is the shape of cancer nodules. However, since the CT scan images are in the form of two-dimensional (2D), it is hard for oncologists to see the full three-dimensional (3D) shape of the cancer nodules. Based on that matter, this study aimed to develop algorithm that can automatically detect and calculate volume of nodules for all slices in 3D reconstruction. 3D reconstruction of cancer nodules is performed through linear interpolation approach. The results of the developed algorithm, tested through a number of slice images from lung CT scan, showed that the approach and algorithm are able to reconstruct nodule shape in 3D and calculate volume automatically. The results obtained are expected to be able to help oncologists provide accurate information of cancer nodules as well as volume and shape of the cancer nodules in 3D surface.

Fingerprint Authenticity Classification Algorithm based-on Distance of Minutiae using Convolutional Neural Network Hariyanto Hariyanto; Sarifuddin Madenda; Sunny Arief Sudiro; Tubagus Maulana Kusuma
InComTech : Jurnal Telekomunikasi dan Komputer Vol 11, No 3 (2021)
Publisher : Department of Electrical Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/incomtech.v11i3.13770

Fingerprint identification systems are vulnerable to attempted authentication fraud by creating fake fingerprints that mimic the live. This paper proposes method to detect whether a fingerprint is live fingerprint or fake fingerprint using Convolutional Neural Network (CNN). We construct a features database of distances among minutiaes of fingerprints, where the distance calculation is based-on Euclidean Distance. Furthermore, the distance features database that has been constructed is classified using the CNN. CNN is a deep learning method designed for machine learning processes so that computers recognize objects in an image and this method has capability classifying an object. The numerical results have shown that the best accuracy achieves 99.38% when the learning rate is 0.001 with the epoch of 100.

Generation of Teeth Caries Features for Human Dental Caries Classification Linda Wahyu Widianti; Sarifuddin Madenda; Johan Harlan; Sunny Sudiro; Farina Pramanik
InComTech : Jurnal Telekomunikasi dan Komputer Vol 11, No 3 (2021)
Publisher : Department of Electrical Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/incomtech.v11i3.13804

Many dental diseases are experienced by humans, one of which is dental caries, there are three types of human dental caries, namely enamel caries, dentin caries and pulp caries. This study contains the detection of caries disease in human teeth using two-dimensional images and radiological results of x-ray periapical radiographs from a test image dataset that has a number of pixels between 374x288 to 672x514 pixels with an image resolution of 96 DPI. The original data of existing dental images was processed using Matlab language to obtain caries features through three stages of the processes: pre-processing stage which are stages of the preprocessing process that converts data from a two-dimensional color image (row/height, column/width) that is stored using three channels Red, Green and Blue (RGB), into a grayscale image with one channel, the process of extracting dental caries features by performing calculations caries area and calculate the distance of the caries area to the nerve canal (pulp), and the process of building learning or reference data from dental caries using 24 radiograph periapical data on molar tooth images processed using Matlab. Dental caries features extraction process and the features learning process to generate references features from dental caries is the main objective of this research. This study result was references features for human dental caries classification.

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