scholarly journals ANALISIS KOMPARASI METODE PERBAIKAN KONTRAS BERBASIS HISTOGRAM EQUALIZATION PADA CITRA MEDIS

2017 ◽  
Vol 8 (1) ◽  
pp. 383-388
Author(s):  
Aditya Akbar Riadi ◽  
Ahmad Abdul Chamid ◽  
Akh Sokhibi
Keyword(s):  
Mean Square Error ◽  
Histogram Equalization ◽  
Mean Square ◽  
X Ray ◽  
Adaptive Histogram Equalization

Citra merupakan gambaran tentang karakteristik suatu obyek menurut kondisi variabel tertentu. Pengolahan citra bertujuan memperbaiki kualitas citra agar mudah diinterpretasi oleh manusia atau mesin (dalam hal ini komputer). Terdapat beberapa operasi di dalam pengolahan citra, salah satunya adalah perbaikan kontras yang pada dasarnya biasa digunakan untuk memunculkan bagian-bagian yang tidak terlihat (hidden feature) pada citra. Hasil citra dari rontgen yang tidak selalu memiliki kualitas citra yang baik, seperti halnya hasil citra x-ray yang terlalu gelap atau ada bagian tulang yang terlihat samar sehingga gambar tidak terlihat jelas. Pada penelitian ini teknik peningkatan citra dengan perbaikan kontras menggunakan metode berbasis Histrogram Equalization. Pada citra medis tersebut dan juga menunjukkan kinerja hasil pengukuran kontrol eror menggunakan Mean Square Error menjelaskan bahwa metode  Contrast Limited Adaptive Histogram Equalization lebih baik dibandingkan dengan metode Histrogram Equalization dan metode Adaptive Histogram Equalization.

scholarly journals Perbandingan Metode Contrast Enhancement pada Citra CT-Scan Kanker Paru-paru

Gravitasi ◽  
2020 ◽  
Vol 19 (2) ◽  
pp. 24-28
Author(s):  
Nurhidayah ◽  
Bannu Abdul Samad ◽  
Bualkar Abdullah
Keyword(s):  
Ct Scan ◽  
Contrast Enhancement ◽  
Mean Square Error ◽  
Signal To Noise Ratio ◽  
Median Filter ◽  
Histogram Equalization ◽  
Mean Square ◽  
Signal To Noise ◽  
Adaptive Histogram Equalization ◽  
Noise Ratio

Abstrak: Di Indonesia kanker paru menjadi penyebab kematian kedua setelah kanker payudara. Angka mortalitas yang cukup tinggi, maka penentuan diagnosis lebih awal memegang peranan yang sangat penting dalam manajemen terapi. Kelemahan CT-Scan dalam mendiagnosa kanker paru-paru disebabkan oleh kontras citra yang rendah dan derau pada citra. Pada penelitian ini akan membandingkan metode contrast enhancement berbasis histogram equalization dan contrast limited adaptive histogram equalization untuk meningkatkan kualitas citra dengan menggunakan software Matlab. Namun, sebelumnya dilakukan reduksi noise dengan menggunakan metode median filter. Kinerja dari setiap metode dihitung dengan mencari nilai MSE (Mean Square Error) dan PSNR (Peak Signal to Noise Ratio) citra. Dari nilai MSE dan PSNR yang di dapatkan diperoleh nilai MSE dan PSNR terbaik pada metode contrast limited adaptive histogram equalization dengan nilai 653,434 dB dan 245,547 dB.

scholarly journals Automatic Leftover Weight Prediction in Tray Box Using Improved Image Segmentation Color Lighting Component

2020 ◽  
Vol 55 (1) ◽  
Author(s):  
Yuita Arum Sari ◽  
Ratih Kartika Dewi ◽  
Jaya Mahar Maligan ◽  
Luthfi Maulana ◽  
Sigit Adinugroho
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Food Waste ◽  
Histogram Equalization ◽  
Mean Square ◽  
Color Channel ◽  
Adaptive Histogram Equalization ◽  
Weight Prediction

The problem of food waste is experienced by many countries, including Indonesia. In the previous Comstock model, estimating food scraps required the expertise of the estimator, but this method has drawbacks because of subjective perspective of even skilled observers. Another weakness occurred when the observers were exhausted, which in turn negatively affected the measurement of leftover estimation. Therefore, in this paper, we propose an approach for automatic weight prediction using image processing in order to minimize the error forecasting caused by humans. Improved lighting component in image segmentation is also utilized. We apply this framework in the tray box images and estimate each compartment. Two types of tray box backgrounds are tested: gray and black backgrounds. The first part of the proposed method takes a lighting component from each color channel of LAB, HSV, YcbCr, YUV, and LUV. Each of those color channels are applied in contrast limited adaptive histogram equalization to adjust the contrast of each image. After that, the Otsu segmentation is applied, and some formulas to calculate leftover automatically are also presented. The result shows remarkable results when applied in the black background of the tray box with root mean square error around 6.67 using an L lighting component of LAB and Y lighting color component as well YcbCr and YUV. The proposed method is good for leftover forecasting since the estimation is not significantly different from one done by human observers.

scholarly journals Cone-Beam 3-D Reconstruction with Double Circular Trajectory

1990 ◽  
Vol 217 ◽  
Author(s):  
Ph. Rizo ◽  
P. Grangeat ◽  
P. Sire ◽  
P. Lemasson ◽  
S. Delageniere
Keyword(s):  
Mean Square Error ◽  
Incomplete Data ◽  
Cone Beam ◽  
Mean Square ◽  
Modulation Transfer ◽  
Circular Trajectory ◽  
X Ray ◽  
Planar Source ◽  
Infinite Line ◽  
Cone Beam Tomography

ABSTRACTIn X-ray cone-beam tomography, the only planar source trajectory that does not produce incomplete data is the infinite line. Such a source trajectory is not experimentally possible. To ensure complete data acquisition with cone-beam radiographs, a set of nonplanar trajectories has been studied. Among the trajectories proposed in the literature, a simple one is a set of two circular trajectories with intersection of the two trajectory axes. The angle between the two axes is related to the maximum aperture of the cone beam. We propose here an exact method for performing this reconstruction using the 3-D Radon transform of the object. The modulation transfer function of this algorithm remains identical to that for the central slice of reconstruction in a single circular trajectory. The relative mean square error for density stays within 2% for an aperture of ±30°. With a single circular trajectory, the relative mean square error may reach 20% at the same aperture. With a double circular trajectory, horizontal artifacts are nearly suppressed.

Assessment of Some Enhancement Methods of Renal X-ray Image

2020 ◽  
Vol 18 (12) ◽  
pp. 01-05
Author(s):  
Salim J. Attia
Keyword(s):  
Image Quality ◽  
Radiation Dosage ◽  
Histogram Equalization ◽  
Quality Factors ◽  
X Ray ◽  
Adaptive Histogram Equalization ◽  
Quality Image ◽  
Enhancement Method ◽  
Spatial Quality

The study focuses on assessment of the quality of some image enhancement methods which were implemented on renal X-ray images. The enhancement methods included Imadjust, Histogram Equalization (HE) and Contrast Limited Adaptive Histogram Equalization (CLAHE). The images qualities were calculated to compare input images with output images from these three enhancement techniques. An eight renal x-ray images are collected to perform these methods. Generally, the x-ray images are lack of contrast and low in radiation dosage. This lack of image quality can be amended by enhancement process. Three quality image factors were done to assess the resulted images involved (Naturalness Image Quality Evaluator (NIQE), Perception based Image Quality Evaluator (PIQE) and Blind References Image Spatial Quality Evaluator (BRISQE)). The quality of images had been heightened by these methods to support the goals of diagnosis. The results of the chosen enhancement methods of collecting images reflected more qualified images than the original images. According to the results of the quality factors and the assessment of radiology experts, the CLAHE method was the best enhancement method.

scholarly journals KLASIFIKASI OSTEOARTHRITIS BERBASIS DUAL TREE COMPLEX WAVELET TRANSFORM DAN CONTRAST LIMITED ADAPTIVE HISTOGRAM EQUALIZATION (CLAHE) MENGGUNAKAN ACTIVE SHAPE MODELS

2020 ◽  
Vol 3 (1) ◽  
pp. 15
Author(s):  
Mohammad Meizaki Fatihin ◽  
Farid Baskoro ◽  
Arif Widodo
Keyword(s):  
Histogram Equalization ◽  
Complex Wavelet Transform ◽  
Shape Model ◽  
X Ray ◽  
Active Shape Models ◽  
Adaptive Histogram Equalization ◽  
Shape Models ◽  
Active Shape ◽  
Osteoarthritis Initiative ◽  
Grade 3

Citra adalah representasi dari informasi yang terkandung di dalamnya sehingga mata manusia dapat menganalisis dan menafsirkan informasi sesuai dengan tujuan yang diharapkan. Salah satu bentuk citra medis adalah citra x-ray. Penelitian ini mengidentifikasi gambar x-ray Osteoarthritis Lutut yang diambil pada berbagai tingkat keparahan, mulai dari KL-Grade 0 hingga KL-Grade 4. Penelitian ini menggunakan metode CLAHE dan DTCWT untuk proses preprosessing dan menggunakan metode Active Shape Model (ASM) untuk proses segmentasi, menggunakan 35 data pelatihan dan 200 data uji dari Osteoarthritis Initiative (OAI). Pengujian citra uji dalam penelitian ini dengan mengekstraksi tekstur citra menggunakan metode GLCM dan segmentasi citra menggunakan ASM, sehingga proses scanning untuk penentuan titik-titik yang berfungsi untuk mengukur ketebalan cartilage. Hasil Ekstraksi tekstur memiliki tingkat akurasi klasifikasi KL-Grade 0 57,5%, KL-Grade 1 memiliki akurasi 33.3%, KL-Grade 2 37,5%, KL-Grade 3 37,5% dan KL-Grade 4 34,3 %. Sedangkan untuk pengukuran ketebalan tulang rawan memiliki akurasi klasifikasi untuk KL-Grade 0 sebesar 62.5%, kemudian KL-Grade 1 sebesar 44.4 %, sedangkan untuk KL-Grade 2 memiliki keberhasilan klasifikasi 60%, kemudian KL-Grade 3 memiliki klasifikasi berhasil dengan benar 70%, dan untuk KL-Grade 4 51.4%.

scholarly journals Pipeline for Advanced Contrast Enhancement (PACE) of Chest X-ray in Evaluating COVID-19 Patients by Combining Bidimensional Empirical Mode Decomposition and Contrast Limited Adaptive Histogram Equalization (CLAHE)

2020 ◽  
Vol 12 (20) ◽  
pp. 8573
Author(s):  
Giulio Siracusano ◽  
Aurelio La Corte ◽  
Michele Gaeta ◽  
Giuseppe Cicero ◽  
Massimo Chiappini ◽  
...  
Keyword(s):  
Contrast Enhancement ◽  
Empirical Mode Decomposition ◽  
Medical Image ◽  
Histogram Equalization ◽  
Post Processing ◽  
X Ray ◽  
Adaptive Histogram Equalization ◽  
Mode Decomposition ◽  
Chest X Ray ◽  
Bidimensional Empirical Mode Decomposition

COVID-19 is a new pulmonary disease which is driving stress to the hospitals due to the large number of cases worldwide. Imaging of lungs can play a key role in the monitoring of health status. Non-contrast chest computed tomography (CT) has been used for this purpose, mainly in China, with significant success. However, this approach cannot be massively used, mainly for both high risk and cost, also in some countries, this tool is not extensively available. Alternatively, chest X-ray, although less sensitive than CT-scan, can provide important information about the evolution of pulmonary involvement during the disease; this aspect is very important to verify the response of a patient to treatments. Here, we show how to improve the sensitivity of chest X-ray via a nonlinear post-processing tool, named PACE (Pipeline for Advanced Contrast Enhancement), combining properly Fast and Adaptive Bidimensional Empirical Mode Decomposition (FABEMD) and Contrast Limited Adaptive Histogram Equalization (CLAHE). The results show an enhancement of the image contrast as confirmed by three widely used metrics: (i) contrast improvement index, (ii) entropy, and (iii) measure of enhancement. This improvement gives rise to a detectability of more lung lesions as identified by two radiologists, who evaluated the images separately, and confirmed by CT-scans. The results show this method is a flexible and an effective approach for medical image enhancement and can be used as a post-processing tool for medical image understanding and analysis.

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