A Comparative Study on Histogram Equalization and Cumulative Histogram Equalization

Image enhancement is a way to improve the appearance of image to human viewers or to image processing system performance. Image Enhancement techniques can be classified into two categories as spatial domain and frequency domain. There arenbsp five image enhancement algorithms in spatial domain using FPGA technology. These algorithms are: median filter, contrast stretching, histogram equalization, negative image transformation and power-law transformation. This review paper presents different methods of histogram equalization. Histogram equalization is a method to enhance an image very efficiently. Histogram equalization methods are Histogram expansion, Local area histogram equalization (LAHE), Cumulative histogram equalization, Par sectioning, odd sectioning

Contrast enhancement by multi-scale adaptive histogram equalization

An approach for contrast enhancement utilizing multi-scale analysis is introduced. Sub-band coefficients were modified by the method of adaptive histogram equalization. To achieve optimal contrast enhancement, the sizes of sub-regions were chosen with consideration to the support of the analysis filters. The enhanced images provided subtle details of tissues that are only visible with tedious contrast/brightness windowing methods currently used in clinical reading. We present results on chest CT data, which shows significant improvement over existing state-of-the-art methods: unsharp masking, adaptive histogram equalization (AHE), and the contrast limited adaptive histogram equalization (CLAHE). A systematic study on 109 clinical chest CT images by three radiologists suggests the promise of this method in terms of both interpretation time and diagnostic performance on different pathological cases. In addition, radiologists observed no noticeable artifacts or amplification of noise that usually appears in traditional adaptive histogram equalization and its variations

Penerapan Metode Contrast Limited Adaptive Histogram Equalization (Clahe) Pada Citra Digital Untuk Memperbaiki Gambar X-ray

Metode Contrast Limited Adaptive Histogram Equalization (CLAHE) bertujuan untuk mengurangi noise dengan cara menentukan kernel matriks dan bekerja dengan menggantikan nilai intensitas setiap pixel citra masukan dengan rata-rata dari nilai pembobotan kernel untuk setiap pixel-pixel tetangganya dan pixel itu sendiri. Pada proses pengurangan noise ukuran kernel sangat mempengaruhi dalam memperoleh hasil kualitas citra. Ukuran kernel yang digunakan penulis pada penelitian ini berukuran 5 x 5. Penelitian ini telah menghasilkan sebuah program aplikasi untuk mengurangi noise dengan metode Contrast Limited Adaptive Histogram Equalization (CLAHE). Citra x-ray uji yang digunakan pada penelitian ini menggunakan citra x-ray hasil scal thorax. Citra tersebut di masukan dan ditampilkan pada program. Kemudian dilakukan proses Contrast Limited Adaptive Histogram Equalization (CLAHE). Maka akan dihasilkan suatu citra sesuai yang diinginkan. Kata Kunci : Clahe, Noise, Clahe, Perbaikan citra, Matlab ABSTRAC The Contrast Limited Adaptive Histogram Equalization (CLAHE) method aims to reduce noise by determining the kernel matrix and work by replacing the intensity value of each input image pixel with an average of the kernel weighting value for each neighboring pixel and pixel itself. In the process of reducing noise the size of the kernel is very influential in obtaining the results of image quality. The size of the kernel used by the author in this study is 5 x 5. This research has produced an application program to reduce noise using the Contrast Limited Adaptive Histogram Equalization (CLAHE) method. The test x-ray image used in this study uses x-ray images of thorax scal. The image is inputted and displayed on the program. Then the Contrast Limited Adaptive Histogram Equalization (CLAHE) process is carried out. Then an image will be produced as desired. Keywords: Cheat, Noise, Cheat, Image Repair, Matla

Comparative analysis of image enhancement techniques for uterine fibroid ultrasound

Background: The Ultrasound image is a vital diagnostic tool in the preliminary clinical assessment of many diseases, especially in Obstetrics and Gynecology. However, poor ultrasound image quality often leads to the inaccurate diagnosis of diseases such as uterine fibroids. Many researchers have proposed various methods for improving ultrasound image quality. Objective: To explore by comparison of four image enhancement techniques, the best approach for the enhancement of uterine fibroid images towards achieving better diagnosis and proper management of the disease..Methodology: The study assessed and compared the performance of four (4) different image enhancement techniques namely; Contrast stretching, Gamma correction, Histogram equalization(HE) and Contrast limited adaptive histogram equalization (CLAHE) on uterine fibroid ultrasound image Twenty (20) Ultrasound images from thedatabasewere downloaded and processed in MATLAB (2015a version) using image processing toolbox. Based on histogram distribution and statistical features (Mean, Standard Deviation and Entropy), the enhanced images were evaluated and compared. Results: The results show that Contrast stretching performed better based on Histogram distribution while CLAHE shows superior performance on Statistical featuresConclusion: Contrast stretching and Contrast limited adaptive histogram equalization (CLAHE)have demostrated good performance in enhancement of uterine fibroid ultrasound ima