scholarly journals Enhancement of digitized X-ray films using Contrast-Limited Adaptive Histogram Equalization (CLAHE)

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 1051
Author(s):  
Wan-Noorshahida Mohd-Isa ◽  
Joel Joseph ◽  
Noramiza Hashim ◽  
Nbhan Salih
Keyword(s):  
Mean Squared Error ◽  
Signal To Noise Ratio ◽  
Correct Diagnosis ◽  
Qualitative Evaluation ◽  
Medical Practitioner ◽  
Histogram Equalization ◽  
X Ray ◽  
Adaptive Histogram Equalization ◽  
Rural Clinics ◽  
Noise Amplification

Background: Rural clinics still have X-ray facilities that produce physical films, which are sent to the nearest hospital for evaluation.  Purchasing digitalization facilities is costly, thus, sending digitized films to the radiologist may be a solution.  This can be achieved via digital photo capture.  However, there can be different output resolutions that may not be optimized for online diagnosis.  This paper investigates if digitized X-ray films can be enhanced using image processing techniques of Contrast-Limited Adaptive Histogram Equalization (CLAHE), Normalized-CLAHE (N-CLAHE) and Min-Max Normalized-CLAHE (MMCLAHE).        Methods: We collected and digitized 21 X-ray films with low, medium, and high resolutions and implemented the CLAHE, N-CLAHE and MMCLAHE image enhancement. These methods introduced a limit to clip the histogram of image intensities so as to reduce any noise amplification before file compression with the Fast Fourier Transform (FFT) and Discrete Cosine Transform (DCT).  Quantitative metrics of the Peak Signal-to-Noise Ratio (PSNR) and Mean-Squared Error (MSE) were used to compare the accuracies between digitized and processed X-ray films.  A qualitative evaluation was performed by a medical practitioner to validate the accuracy of enhanced digitized X-ray.  Results: It had been found that both CLAHE and MMCLAHE provided good average PSNR values of 31dB - 32dB and produced low MSE values compared to N-CLAHE.  The results of qualitative evaluation attained 89.9% correct diagnosis on nine randomly selected images.  Generally, the evaluation indicated that the results fulfill the acceptable criteria for further evaluation and there seemed to be no pathological differences observed. Conclusion: This paper presented a proof of concept on an implementation of the CLAHE technique and its variations on digitized X-ray films.  This paper had shown potential improvements with the proposed enhancement methods that may contribute to an increase efficiency in healthcare processes at rural clinics.

scholarly journals IMAGE ENHACEMENT PADA CITRA GESTUR TANGAN MENGGUNAKAN CONTRAST LIMITED ADAPTIVE HISTOGRAM EQUALIZATION

JOUTICA ◽  
2021 ◽  
Vol 6 (2) ◽  
pp. 454
Author(s):  
Natanael Putra Yustiantara
Keyword(s):  
Mean Squared Error ◽  
Signal To Noise Ratio ◽  
Histogram Equalization ◽  
Closed Circuit ◽  
Signal To Noise ◽  
Closed Circuit Television ◽  
Adaptive Histogram Equalization ◽  
Squared Error ◽  
Noise Ratio

Image Enhacement merupakan proses perbaikan kualitas citra yang dilakukan dengan menggunakan beberapa metode. Citra yang paling sering dilakukan perbaikan kualitas adalah citra digital. Citra digital sering digunakan pada pengolahan citra biometrik, pengenalan wajah, pengenalan tanda tangan, bahkan permasalahan pada Closed Circuit Television (CCTV). Penelitian ini bertujuan untuk memberikan perbedaan hasil proses image enhacement pada gambar yang telah tertangkap oleh CCTV. Penelitian ini menggunakan 3 buah metode yaitu, Histogram Equalization (HE), Adaptive Histogram Equalization (AHE), dan Contrast Limited Adaptive Histogram Equalization (CLAHE) untuk melakukan perbaikan citra, sedangkan objek yang akan digunakan pada penelitian ini adalah citra gesture tangan. Dari hasil penelitian ini dapat dilihat bahwa Nilai MSE (Mean Squared Error) yang mendekati angka 0 adalah gambar yang menggunakan metode CLAHE (Contrast Limited Adaptive Histogram Equalization) dengan nilai sebesar 653.5. Untuk nilai PSNR (Peak Signal to Noise Ratio) sendiri nilai yang paling besar yaitu 29.9783476895 dengan menggunakan metode CLAHE.

Image Quality Enhancing by Efficient Histogram Equalization

2014 ◽  
Vol 2 (2) ◽  
pp. 47-58
Author(s):  
Ismail Sh. Baqer
Keyword(s):  
Image Quality ◽  
Contrast Enhancement ◽  
Mean Squared Error ◽  
Signal To Noise Ratio ◽  
Histogram Equalization ◽  
Gray Level ◽  
Signal To Noise ◽  
Squared Error ◽  
Noise Ratio

A two Level Image Quality enhancement is proposed in this paper. In the first level, Dualistic Sub-Image Histogram Equalization DSIHE method decomposes the original image into two sub-images based on median of original images. The second level deals with spikes shaped noise that may appear in the image after processing. We presents three methods of image enhancement GHE, LHE and proposed DSIHE that improve the visual quality of images. A comparative calculations is being carried out on above mentioned techniques to examine objective and subjective image quality parameters e.g. Peak Signal-to-Noise Ratio PSNR values, entropy H and mean squared error MSE to measure the quality of gray scale enhanced images. For handling gray-level images, convenient Histogram Equalization methods e.g. GHE and LHE tend to change the mean brightness of an image to middle level of the gray-level range limiting their appropriateness for contrast enhancement in consumer electronics such as TV monitors. The DSIHE methods seem to overcome this disadvantage as they tend to preserve both, the brightness and contrast enhancement. Experimental results show that the proposed technique gives better results in terms of Discrete Entropy, Signal to Noise ratio and Mean Squared Error values than the Global and Local histogram-based equalization methods

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 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 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 A Novel Image Processing Methodology for X-ray Image Compression and Enhancement

2022 ◽  
pp. 1-8
Author(s):  
Calvin Omind Munna
Keyword(s):  
Image Processing ◽  
Mean Squared Error ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
X Ray ◽  
Squared Error ◽  
Structure Similarity Index ◽  
Structure Similarity ◽  
Clinical Pictures

Currently, there a growing demand of data produced and stored in clinical domains. Therefore, for effective dealings of massive sets of data, a fusion methodology needs to be analyzed by considering the algorithmic complexities. For effective minimization of the severance of image content, hence minimizing the capacity to store and communicate data in optimal forms, image processing methodology has to be involved. In that case, in this research, two compression methodologies: lossy compression and lossless compression were utilized for the purpose of compressing images, which maintains the quality of images. Also, a number of sophisticated approaches to enhance the quality of the fused images have been applied. The methodologies have been assessed and various fusion findings have been presented. Lastly, performance parameters were obtained and evaluated with respect to sophisticated approaches. Structure Similarity Index Metric (SSIM), Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR) are the metrics, which were utilized for the sample clinical pictures. Critical analysis of the measurement parameters shows higher efficiency compared to numerous image processing methods. This research draws understanding to these approaches and enables scientists to choose effective methodologies of a particular application.

scholarly journals De-noising of MRI Images in Wavelet Domain

2019 ◽  
Vol 8 (4) ◽  
pp. 1947-1949
Keyword(s):  
Signal To Noise Ratio ◽  
Histogram Equalization ◽  
The Body ◽  
Medical Procedure ◽  
Wavelet Domain ◽  
Radio Waves ◽  
Resonance Imaging ◽  
Adaptive Histogram Equalization ◽  
Magnetic Resonance Imaging Mri

Magnetic resonance imaging (MRI) is a diagnostic medical procedure that utilizes solid attractive fields and radio waves to deliver definite pictures of within the body. Extensive research has been completed into whether the attractive fields and radio waves utilized during MRI sweeps could represent a hazard to the human body. No proof has been found to propose there's a hazard, which means MRI outputs are one of the most secure restorative methodology accessible. MRI has several advantages which make it ideal in numerous situations, in particular, it can identify small changes of structures inside the body. The disadvantage is the noise that degrades the quality of the image. A threestep processing algorithm is proposed to reduce this noise. Here, first it includes soft thresholding in wavelet domain where the original image is divided into blocks that do not overlap. Then it includes restoration of the object boundaries and texture which are lost as a result of the first step and finally enhancing the image using CLAHE (Contrast Limiting Adaptive Histogram Equalization). It is then analyzed using the error parameters like peak signal to noise ratio and mean square error.

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.

Optimization Before Biomedical Image Compression Using CLAHE and DCS

2018 ◽  
Vol 7 (3.27) ◽  
pp. 236
Author(s):  
Satyawati S. Magar ◽  
Bhavani Sridharan
Keyword(s):  
Medical Imaging ◽  
Image Compression ◽  
Image Enhancement ◽  
Compression Ratio ◽  
Signal To Noise Ratio ◽  
Histogram Equalization ◽  
Signal To Noise ◽  
Biomedical Image ◽  
Adaptive Histogram Equalization ◽  
Noise Ratio

In current years, improving the Compression Ratio (CR) in medical imaging is essential and becomes big challenge in the field of biomedical. In that direction we have done optimization before biomedical image compression. For the same we have used the image enhancement techniques. For the enhancement of an image we have used Contrast Limited Adaptive Histogram Equalization (CLAHE) and Decorrelation Stretch (DCS) algorithms. By optimizing an image before compression we have achieved better Compression Ratio (CR) and Peak Signal to Noise Ratio (PSNR) than existing methods of an image compression. Mainly results are compared with Oscillation Concept method of an image compression with and without optimization.  

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