Implementasi Metode Gaussian dan Median Filtering dalam Penghilangan Noise pada Citra

Meningkatnya angka kriminalitas menjadikan salah satu faktor dipasangnya CCTV pada beberapa sudut area oleh beberapa lembaga sebagai bentuk pengawasan, salah satunya yang telah dilakukan oleh Kementerian Perhubungan Republik Indonesia. Pengawasan melalui CCTV sering kali menghadapi gangguan, seperti hasil citra ber-noise yang menghambat proses pengidentifikasian suatu objek yang tertangkap CCTV. Oleh karena itu, penulis mencoba untuk melakukan implementasi metode Gaussian Filtering dan Median Filtering sebagai upaya dalam menghilangkan noise pada citra yang dihasilkan oleh CCTV. Implementasi yang akan dilakukan pada penelitian ini diawali dengan melakukan input data yang berupa citra hasil screen capture CCTV, kemudan dilakukan konversi dari citra berwarna menjadi citra greyscale. Tahap selanjutnya adalah melakukan penghilangan noise menggunakan metode Gaussian Filtering dan Median Filtering. Mean Square Error (MSE) dan Peak Signal to Noise Ratio (PSNR) digunakan dalam pengujian. Dapat disimpulkan dari penelitian ini, Median Filtering lebih efektif dalam melakukan penghilangan noise dari pada Gaussian Filtering. Hal ini dibuktikan dari 20 percobaan penghilangan noise menggunakan Median Filtering, 80% citra yang diproses menghasilkan nilai PSNR yang lebih besar daripada nilai PSNR citra dengan noise dan mengartikan jika citra yang diproses mendekati citra asli (citra tanpa noise). Sedangkan dari dari 20 percobaan penghilangan noise menggunakan Gaussian Filtering hanya 50% citra yang diproses menghasilkan nilai PSNR yang lebih besar daripada nilai PSNR citra dengan noise. Selanjutnya, untuk nilai standar deviasi terbaik penghilangan noise pada citra adalah ketika ada pada nilai 2 dengan rerata persentase penurunan noise sebesar 1,73%. Kata Kunci: CCTV, Citra, Pengolahan Citra, Noise, Gaussian Filtering, Median Filtering.

Mesenteric cyst detection and segmentation by multiple K-means clustering and iterative Gaussian filtering

<span lang="EN-US">In this article a fully automated machine-vision technique for the detection and segmentation of mesenteric cysts in computed tomography (CT) images of the abdominal space is presented. The proposed technique involves clustering, filtering, morphological operations and evaluation processes to detect and segment mesenteric cysts in the abdomen regardless of their texture variation and location with respect to other surrounding abdominal organs. The technique is comprised of various processing phases, which include K-means clustering, iterative Gaussian filtering, and an evaluation of the segmented regions using area-normalized histograms and Euclidean distances. The technique was tested using 65 different abdominal CT scan images. The results showed that the technique was able to detect and segment mesenteric cysts and achieved 99.31%, 98.44%, 99.84%, 98.86% and 99.63% for precision, recall, specificity, dice score coefficient and accuracy respectively as quantitative performance measures which indicate very high segmentation accuracy.</span>

Classification of Initial Stages of Alzheimer’s Disease through Pet Neuroimaging Modality and Deep Learning: Quantifying the Impact of Image Filtering Approaches

Alzheimer’s disease (AD) is a leading health concern affecting the elderly population worldwide. It is defined by amyloid plaques, neurofibrillary tangles, and neuronal loss. Neuroimaging modalities such as positron emission tomography (PET) and magnetic resonance imaging are routinely used in clinical settings to monitor the alterations in the brain during the course of progression of AD. Deep learning techniques such as convolutional neural networks (CNNs) have found numerous applications in healthcare and other technologies. Together with neuroimaging modalities, they can be deployed in clinical settings to learn effective representations of data for different tasks such as classification, segmentation, detection, etc. Image filtering methods are instrumental in making images viable for image processing operations and have found numerous applications in image-processing-related tasks. In this work, we deployed 3D-CNNs to learn effective representations of PET modality data to quantify the impact of different image filtering approaches. We used box filtering, median filtering, Gaussian filtering, and modified Gaussian filtering approaches to preprocess the images and use them for classification using 3D-CNN architecture. Our findings suggest that these approaches are nearly equivalent and have no distinct advantage over one another. For the multiclass classification task between normal control (NC), mild cognitive impairment (MCI), and AD classes, the 3D-CNN architecture trained using Gaussian-filtered data performed the best. For binary classification between NC and MCI classes, the 3D-CNN architecture trained using median-filtered data performed the best, while, for binary classification between AD and MCI classes, the 3D-CNN architecture trained using modified Gaussian-filtered data performed the best. Finally, for binary classification between AD and NC classes, the 3D-CNN architecture trained using box-filtered data performed the best.

Gaussian Filtering Method to Remove Noise in Images

Capturing every moment is not taboo in this era. One way to capture the moment is to use a photo, but the results are often unsatisfactory. Noise, is one of the many causes of unsatisfactory results. Noise is a disturbance caused by digital data storage received by the image data receiver which can interfere with image quality. Noise can be caused by physical (optical) disturbances in the image capturing device, such as dust on the camera lens or due to improper processing. To get rid of this noise, you can use various methods, of which Gaussian Filtering is one of them. In this research, we will implement it using Matlab. The type of file used is a photo that has a jpg format and has noise above 75%. After doing image processing, it shows the results of the image which initially has noise and after the image quality improvement process is carried out, the image quality is clearer and the noise decreases.