scholarly journals Improving radiographic image contrast using multi layers of histogram equalization technique

2021 ◽  
Vol 10 (1) ◽  
pp. 151
Author(s):  
Farah F. Alkhalid ◽  
Ahmed Mudher Hasan ◽  
Ahmed A. Alhamady
Keyword(s):  
Decision Analysis ◽  
Nerve Root ◽  
Histogram Equalization ◽  
Image Contrast ◽  
High Contrast ◽  
Radiographic Image ◽  
X Ray ◽  
Adaptive Histogram Equalization ◽  
Suggested Technique ◽  
Equalization Technique

<span id="docs-internal-guid-43432eef-7fff-9949-6deb-865191ff0740"><span>Usually, X-ray image has distortion in many parts because it is focusing on bones rather than other, However, when dentist needs to make decision analysis, he does that by using X-ray and many opinions can be judged by looking closely on it like (inflammation, infection, tooth nerve, root of the tooth…). This paper proposes on new suggested technique by applying multilayers of histogram equalization (HE) and contrast limited adaptive histogram equalization (CLAHE) in order to make high contrast of X-ray, this technique provides very satisfied results and smooth intensity which leads to high clear X-ray image, by using Python3 and OpenCV.</span></span>

scholarly journals Study on Construction of a Medical X-Ray Direct Digital Radiography System and Hybrid Preprocessing Methods

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Yong Ren ◽  
Sheng Wu ◽  
Mijian Wang ◽  
Zhongjie Cen
Keyword(s):  
Contrast Enhancement ◽  
Digital Radiography ◽  
Histogram Equalization ◽  
Image Contrast ◽  
X Ray ◽  
Adaptive Histogram Equalization ◽  
Radiography System ◽  
Image Contrast Enhancement ◽  
Flat Field ◽  
Enhancement Algorithm

We construct a medical X-ray direct digital radiography (DDR) system based on a CCD (charge-coupled devices) camera. For the original images captured from X-ray exposure, computer first executes image flat-field correction and image gamma correction, and then carries out image contrast enhancement. A hybrid image contrast enhancement algorithm which is based on sharp frequency localization-contourlet transform (SFL-CT) and contrast limited adaptive histogram equalization (CLAHE), is proposed and verified by the clinical DDR images. Experimental results show that, for the medical X-ray DDR images, the proposed comprehensive preprocessing algorithm can not only greatly enhance the contrast and detail information, but also improve the resolution capability of DDR system.

scholarly journals Local Contrast Enhancement Utilizing Bidirectional Switching Equalization of Separated and Clipped Subhistograms

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Haidi Ibrahim ◽  
Seng Chun Hoo
Keyword(s):  
Contrast Enhancement ◽  
Histogram Equalization ◽  
Processing Technique ◽  
Image Contrast ◽  
Local Contrast ◽  
Image Contrast Enhancement ◽  
Enhancement Method ◽  
Equalization Technique ◽  
Histogram Bins ◽  
Almost All

Digital image contrast enhancement methods that are based on histogram equalization technique are still useful for the use in consumer electronic products due to their simple implementation. However, almost all the suggested enhancement methods are using global processing technique, which does not emphasize local contents. Therefore, this paper proposes a new local image contrast enhancement method, based on histogram equalization technique, which not only enhances the contrast, but also increases the sharpness of the image. Besides, this method is also able to preserve the mean brightness of the image. In order to limit the noise amplification, this newly proposed method utilizes local mean-separation, and clipped histogram bins methodologies. Based on nine test color images and the benchmark with other three histogram equalization based methods, the proposed technique shows the best overall performance.

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%.

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