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Journal articles on the topic 'Gray-level histogram'

Author: Grafiati
Published: 3 June 2025
Last updated: 20 July 2025

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1

Tan, Yanli, and Yongqiang Zhao. "A Fast Otsu Thresholding Method Based on an Improved 2D Histogram." International Journal of Circuits, Systems and Signal Processing 15 (August 12, 2021): 953–59. http://dx.doi.org/10.46300/9106.2021.15.102.

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The regional division of a traditional 2D histogram is difficult to obtain satisfactory image segmentation results. Based on the gray level-gradient 2D histogram, we proposed a fast 2D Otsu method based on integral image. In this method, the average gray level is replaced by the gray level gradient in the neighborhood of pixels, and the edge features of the image are extracted according to the gray level difference between adjacent pixels to improve the segmentation effect. Calculating the integral image from the two-dimensional histogram reduces the computational complexity of searching the o
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Chang, Jeng-Horng, Kuo-Chin Fan, and Yang-Lang Chang. "Multi-modal gray-level histogram modeling and decomposition." Image and Vision Computing 20, no. 3 (2002): 203–16. http://dx.doi.org/10.1016/s0262-8856(01)00095-6.

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3

Nalbant, MO, and E. Inci. "The Efficiency of Gray-Level Ultrasound Histogram Analysis in Patients with Supraspinatus Tendinopathy." Nigerian Journal of Clinical Practice 26, no. 11 (2023): 1709–15. http://dx.doi.org/10.4103/njcp.njcp_325_23.

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ABSTRACT Background: Musculoskeletal ultrasonography is a viable substitute for magnetic resonance imaging (MRI) that offers advantages in terms of time efficiency and cost-effectiveness. The gray-level histogram is a tool used to depict the distribution of pixel gray levels that provide quantitative data. Aim: The objective of our research was to establish a threshold value for ultrasonography-measured supraspinatus tendon gray-level values by comparing patients with tendinopathy to those without. Materials and Methods: This study comprised a cohort of 271 individuals, consisting of 124 patie
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Yousuf, M. A., and M. R. H. Rakib. "An Effective Image Contrast Enhancement Method Using Global Histogram Equalization." Journal of Scientific Research 3, no. 1 (2010): 43. http://dx.doi.org/10.3329/jsr.v3i1.5299.

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Image enhancement is one of the most important issues in low-level image processing. Histograms are the basis for numerous spatial domain processing techniques. In this paper, we present a simple and effective method for image contrast enhancement based on global histogram equalization. In this method, at first input image is normalized by making the minimum gray level value to 0. Then the probability of each grey level is calculated from the available ROI grey levels. Finally, histogram equalization is performed on the input image based on the calculated probability density (or distribution)
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Zheng, Xiulian, Hong Ye, and Yinggan Tang. "Image Bi-Level Thresholding Based on Gray Level-Local Variance Histogram." Entropy 19, no. 5 (2017): 191. http://dx.doi.org/10.3390/e19050191.

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Prasad, M. Seetharama, C. Naga Raju, and L. S. S. Reddy. "Fuzzy Entropic Thresholding Using Gray Level Spatial Correlation Histogram." i-manager's Journal on Software Engineering 6, no. 2 (2011): 20–30. http://dx.doi.org/10.26634/jse.6.2.2894.

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7

Kanika, Kapoor, and Arora Shaveta. "COLOUR IMAGE ENHANCEMENT BASED ON HISTOGRAM EQUALIZATION." Electrical & Computer Engineering: An International Journal (ECIJ) 4, no. 3 (2015): 73–82. https://doi.org/10.5281/zenodo.3593819.

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Histogram equalization is a nonlinear technique for adjusting the contrast of an image using its histogram. It increases the brightness of a gray scale image which is different from the mean brightness of the original image. There are various types of Histogram equalization techniques like Histogram Equalization, Contrast Limited Adaptive Histogram Equalization, Brightness Preserving Bi Histogram Equalization, Dualistic Sub Image Histogram Equalization, Minimum Mean Brightness Error Bi Histogram Equalization, Recursive Mean Separate Histogram Equalization and Recursive Sub Image Histogram Equa
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Lv, Ying. "Typhoon Cloud Tracking by Kalman Filter." Applied Mechanics and Materials 58-60 (June 2011): 2487–92. http://dx.doi.org/10.4028/www.scientific.net/amm.58-60.2487.

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Typhoon cloud has its changeability, so it is difficult to track and predict compared with the rigid targets. Region of interest (ROI) and reference region were selected by using interactive methods. Bezier curve is used to smooth the gray level histogram of ROI and obtain Bezier histogram. The gray level value which is corresponding to the valley of the Bezier histogram is used to segment the ROI in order to get the tracking target. And target parameters could be predicted by using Kalman filter, then getting the moving track of the target. Experimental results show that the proposed algorith
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Kadhum, Zainab Abdulrazzaq. "Equalize The Histogram Equalization for Image enhancement." Journal of Kufa for Mathematics and Computer 1, no. 5 (2012): 14–21. http://dx.doi.org/10.31642/jokmc/2018/010502.

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Histogram Equalization is one of the technique most commonly used in contrast enhancement. it tends to change the mean brightness of the image to the middle level of the gray level range. However, In this paper, a simple contrast enhancement technique based on conventional histogram equalization algorithm is proposed. This Equalize The histogram equalization technique which takes control over the effect of  histogram equalization technique so that it performs the enhancement of an image
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Baqer, Ismail Sh. "Image Quality Enhancing by Efficient Histogram Equalization." Wasit Journal of Engineering Sciences 2, no. 2 (2014): 47–58. http://dx.doi.org/10.31185/ejuow.vol2.iss2.29.

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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-N
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Han, Dian Yuan. "Frog and Haze Image Enhancement Using Improved Histogram Equalization Method in HIS Color Space." Applied Mechanics and Materials 687-691 (November 2014): 3671–74. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.3671.

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This paper concerns the problem of frog and haze image enhancement. Images are often degreed due to the fog and haze condition. In this paper, an image enhancement method by using improved histogram equalization in HIS color space was put forward. Firstly, the image was transformed from RGB to HIS color space. Then the S and I components were treated with improved histogram equalization separately. When judging whether a gray level was to be merged with another, the weight coefficients with increased step were assigned to these low frequency gray levels according to their distance to the curre
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Ye, Bowen, Sun Jin, Bing Li, Shuaiyu Yan, and Deng Zhang. "Dual Histogram Equalization Algorithm Based on Adaptive Image Correction." Applied Sciences 13, no. 19 (2023): 10649. http://dx.doi.org/10.3390/app131910649.

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For the visual measurement of moving arm holes in complex working conditions, a histogram equalization algorithm can be used to improve image contrast. To lessen the problems of image brightness shift, image over-enhancement, and gray-level merging that occur with the traditional histogram equalization algorithm, a dual histogram equalization algorithm based on adaptive image correction (AICHE) is proposed. To prevent luminance shifts from occurring during image equalization, the AICHE algorithm protects the average luminance of the input image by improving upon the Otsu algorithm, enabling it
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ZHANG Lei, 张磊, 李亚妮 LI Ya-ni, 陆吕晨 LU Lv-chen, and 吕国强 LV Guo-qiang. "Backlight dimming algorithm based on histogram and dominant gray level." Chinese Journal of Liquid Crystals and Displays 29, no. 6 (2014): 1090–95. http://dx.doi.org/10.3788/yjyxs20142906.1090.

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14

Rajinikanth, V., J. P. Aashiha, and A. Atchaya. "Gray-Level Histogram based Multilevel Threshold Selection with Bat Algorithm." International Journal of Computer Applications 93, no. 16 (2014): 1–8. http://dx.doi.org/10.5120/16296-6035.

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15

Xiao, Yang. "New entropic thresholding approach using gray-level spatial correlation histogram." Optical Engineering 49, no. 12 (2010): 127007. http://dx.doi.org/10.1117/1.3526333.

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16

Zheng, Xiulian, Yinggan Tang, and Wenzhao Hu. "Image thresholding based on gray level-fuzzy local entropy histogram." IEEJ Transactions on Electrical and Electronic Engineering 13, no. 4 (2017): 627–31. http://dx.doi.org/10.1002/tee.22609.

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Santhi, K., Wahida Banu, and R. Dhanasekaran. "Contrast Enhanced for Microstructure of Steel Materials and Engine Components." Advanced Materials Research 984-985 (July 2014): 1375–79. http://dx.doi.org/10.4028/www.scientific.net/amr.984-985.1375.

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This paper analyses the contrast of qualitative and quantitative of piston and steel microstructure images. A simple discrimination metric (DMHE) is developed to avoid the drawbacks of conventional histogram equalization for gray scale images. The proposed technique uses both global and local information to remap the intensity levels that help to improve the image contrast. The original histogram is divided into sub-histograms with respect to the mean value. Discrimination metrics are used so that high contrast per pixel between real image and upgraded image is obtained. The simulation results
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18

Maeda, Kazuo, PE Kihaile, T. Ito, M. Utsu, N. Yamamoto, and M. Serizawa. "Tissue Characterization with Gray-level Histogram Width in Obstetrics and Gynecology." Donald School Journal of Ultrasound in Obstetrics and Gynecology 11, no. 1 (2017): 7–10. http://dx.doi.org/10.5005/jp-journals-10009-1499.

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ABSTRACT Aim Clinical ultrasound tissue characterization, using usual B-mode devices. Materials and methods Malignant neoplasia in ovary, uterine cervix, and endometrium; placental intervillous space fibrin deposit; fetal growth restriction; fetal brain, fetal lung immaturity; meconium-stained amniotic fluid and healthy adult liver; Tissue was characterized by gray-level histogram width (GLHW) divided by full gray scale length. Results Malignant GLHW was higher than in benign one (it was malignant if the GLHW was 50% or more in ovary, uterine cervix, and endometrium). The GLHW of placental fib
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19

S, Sanjayprabu, Sathish Kumar R, Saeid Jafari, and Karthikamani R. "On Performance Analysis Of Diabetic Retinopathy Classification." ELCVIA Electronic Letters on Computer Vision and Image Analysis 22, no. 2 (2024): 12–25. http://dx.doi.org/10.5565/rev/elcvia.1677.

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This paper describes the Classification of bulk OCT retinal fundus images of normal and diabetic retinopathy using the Intensity histogram features, Gray Level Co-Occurrence Matrix (GLCM), and the Gray Level Run Length Matrix (GLRLM) feature extraction techniques. Three features—Intensity histogram features, GLCM, and GLRLM were taken and, that features were compared fairly. A total of 301 bulk OCT retinal fundus color images were taken for two different varieties which are normal and diabetic retinopathy. For classification and feature extraction, a filtered image output based on a fourth-ord
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Maji, Pradipta, Malay K. Kundu, and Bhabatosh Chanda. "Second Order Fuzzy Measure and Weighted Co-Occurrence Matrix for Segmentation of Brain MR Images." Fundamenta Informaticae 88, no. 1-2 (2008): 161–76. https://doi.org/10.3233/fun-2008-881-207.

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A robust thresholding technique is proposed in this paper for segmentation of brain MR images. It is based on the fuzzy thresholding techniques. Its aim is to threshold the gray level histogram of brain MR images by splitting the image histogram into multiple crisp subsets. The histogram of the given image is thresholded according to the similarity between gray levels. The similarity is assessed through a second order fuzzy measure such as fuzzy correlation, fuzzy entropy, and index of fuzziness. To calculate the second order fuzzy measure, a weighted co-occurrence matrix is presented, which e
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Aarthi D. "Optimizing Medical Image Classification Using Diverse Feature Extraction Methods for Brain Tumor." Panamerican Mathematical Journal 35, no. 2s (2024): 340–52. https://doi.org/10.52783/pmj.v35.i2s.2638.

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Detecting brain tumor early and accurately is crucial for effective treatment and better patient outcomes. Three feature extraction methods are used in this study: Gray Level Run Length Matrix (GLRLM), Gray Level Co-occurrence Matrix (GLCM), and Gray Level Histogram Features (GLHF)—to classify MRI images of the brain. GLCM measures the relationship between pixel intensities to capture texture information, while GLRLM finds patterns of pixels with similar gray levels, showing areas of texture. The histogram method summarizes the overall intensity in the image, highlighting differences between n
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Almotairi, Khaled. "A Global Two-Stage Histogram Equalization Method for Gray-Level Images." Journal of ICT Research and Applications 14, no. 2 (2020): 95. http://dx.doi.org/10.5614/10.5614/itbj.ict.res.appl.2020.14.2.1.

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Yulia, Rantika, Alda Cendekia Siregar, and Barry Ceasar Octariadi. "Identifikasi Ular Menggunakan Metode Gray Level Co-Occurrence Matrix Dan Histogram." Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi 13, no. 2 (2024): 1053. https://doi.org/10.35889/jutisi.v13i2.1964.

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24

Zhu, Youlian, and Cheng Huang. "An Adaptive Histogram Equalization Algorithm on the Image Gray Level Mapping." Physics Procedia 25 (2012): 601–8. http://dx.doi.org/10.1016/j.phpro.2012.03.132.

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Serizawa, M., and K. Maeda. "P19Evaluation of fetal lung maturity by the gray level histogram width." Ultrasound in Obstetrics and Gynecology 16 (October 2000): 71. http://dx.doi.org/10.1046/j.1469-0705.2000.00004-1-19.x.

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Husain, Nursuci Putri, and Nurseno Bayu Aji. "Pemanfaatan Histogram Equalization pada Local Tri Directional Pattern untuk Sistem Temu Kembali Citra." SPECTA Journal of Technology 4, no. 1 (2020): 49–58. http://dx.doi.org/10.35718/specta.v4i1.164.

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Abstract
 
 Local tri-directional pattern (LtriDP) is a method of extracting local intensity features from each pixel based on direction. However, this method has not been able to provide good performance in extracting features for image retrieval. One reason that makes image retrieval performance worse is the effect of lighting. Lighting can cause large variations between images. This study proposed utilization of Histogram Equalization (HE). Histogram equalization is a functional method of stretching gray degrees and expanding image contrast. This will make variations in the gray l
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Yan, Peng, Wu Zhan, and Ou Yang Min-Zi. "Histogram Equalization Based on Rough Set." Applied Mechanics and Materials 182-183 (June 2012): 1844–48. http://dx.doi.org/10.4028/www.scientific.net/amm.182-183.1844.

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In digital image processing, classical histogram equalization produce the loss of image information the caused by gray level of the output image may be too much merged. This paper mainly based on the concepts of the set approximate, classification approximate measurement and importance in the rough set theory, divided the appropriate boundary of the set, proposed an improved histogram equalization method, thus effectively solved the problem, gave the experimental simulation confirmation.
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Shin, Young Gyung, Jaeheung Yoo, Hyeong Ju Kwon, et al. "Histogram and gray level co-occurrence matrix on gray-scale ultrasound images for diagnosing lymphocytic thyroiditis." Computers in Biology and Medicine 75 (August 2016): 257–66. http://dx.doi.org/10.1016/j.compbiomed.2016.06.014.

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Zeng, Ming, Youfu Li, Qinghao Meng, Ting Yang, and Jian Liu. "Improving histogram-based image contrast enhancement using gray-level information histogram with application to X-ray images." Optik 123, no. 6 (2012): 511–20. http://dx.doi.org/10.1016/j.ijleo.2011.05.017.

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Magudeeswaran, V., and C. G. Ravichandran. "Fuzzy Logic-Based Histogram Equalization for Image Contrast Enhancement." Mathematical Problems in Engineering 2013 (2013): 1–10. http://dx.doi.org/10.1155/2013/891864.

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Fuzzy logic-based histogram equalization (FHE) is proposed for image contrast enhancement. The FHE consists of two stages. First, fuzzy histogram is computed based on fuzzy set theory to handle the inexactness of gray level values in a better way compared to classical crisp histograms. In the second stage, the fuzzy histogram is divided into two subhistograms based on the median value of the original image and then equalizes them independently to preserve image brightness. The qualitative and quantitative analyses of proposed FHE algorithm are evaluated using two well-known parameters like ave
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Liantoni, Febri, and Agus Santoso. "PERBAIKAN KONTRAS CITRA MAMMOGRAM PADA KLASIFIKASI KANKER PAYUDARA BERDASARKAN FITUR GRAY-LEVEL CO-OCCURRENCE MATRIX." SINTECH (Science and Information Technology) Journal 3, no. 1 (2020): 46–51. http://dx.doi.org/10.31598/sintechjournal.v3i1.528.

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In this era to recognize breast tumors can be based on mammogram images. This method will expedite the process of recognition and classification of breast cancer. This research was conducted classification techniques of breast cancer using mammogram images. The proposed model targets classification studies for cases of malignant, and benign cancer. The research consisted of five main stages, preprocessing, histogram equalization, convolution, feature extraction, and classification. For preprocessing cropping the image using region of interest (ROI), for convolution, median filter and histogram
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El-Sayed, Mohamed A. "Algorithm based on Histogram and Entropy for Edge Detection in Gray Level Images." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 11, no. 1 (2013): 2207–15. http://dx.doi.org/10.24297/ijct.v11i1.1192.

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Edge detection and feature extraction are widely used in image processing and computer vision applications. Most of the traditional methods for edge detection are based on the first and second order derivatives of gray levels of the pixels of the original image utilizing 2D spatial convolution masks to approximate the derivative. In this paper we present an algorithm for edge detection in gray level images. The main objective is to solve the previous problem of traditional methods with generate suitable quality of edge detection. Our new algorithm is based on two definitions of entropy: Shann
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Maeda, K., M. Utsu, N. Yamamoto, M. Serizawa, and T. Ito. "Clinical tissue characterization with gray level histogram width in obstetrics and gynecology." Ultrasound Review of Obstetrics and Gynecology 2, no. 2 (2002): 124–28. http://dx.doi.org/10.3109/14722240208500466.

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Serizawa, Mariko, and Kazuo Maeda. "Noninvasive Fetal Lung Maturity Prediction Based on Ultrasonic Gray Level Histogram Width." Ultrasound in Medicine & Biology 36, no. 12 (2010): 1998–2003. http://dx.doi.org/10.1016/j.ultrasmedbio.2010.08.011.

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Milles, Julien, Yue Min Zhu, Gérard Gimenez, Charles R. G. Guttmann, and Isabelle E. Magnin. "MRI intensity nonuniformity correction using simultaneously spatial and gray-level histogram information." Computerized Medical Imaging and Graphics 31, no. 2 (2007): 81–90. http://dx.doi.org/10.1016/j.compmedimag.2006.11.001.

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Maeda, K., M. Utsu, N. Yamamoto, M. Serizawa, and T. Ito. "Clinical tissue characterization with gray level histogram width in obstetrics and gynecology." Ultrasound Review of Obstetrics & Gynecology 2, no. 2 (2002): 124–28. http://dx.doi.org/10.1080/14722240208500466.

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ITO, Takashi, Koichi ISHIHARA, Imari DEURA, Chieko KATAGIRI, and Kazuo MAEDA. "Tissue characterization of uterine myometrium using the ultrasound gray-level histogram width." Journal of Medical Ultrasonics 34, no. 4 (2007): 189–92. http://dx.doi.org/10.1007/s10396-007-0153-z.

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Xue, Guang Hui, Bao Hua Hu, Xin Ying Zhao, Er Meng Liu, and Wei Jian Ding. "Study on Characteristic Extraction of Coal and Rock at Mechanized Top Coal Caving Face Based on Image Gray Scale." Applied Mechanics and Materials 678 (October 2014): 193–96. http://dx.doi.org/10.4028/www.scientific.net/amm.678.193.

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A method on the feature extraction of coal and rock character recognition was mainly put forward based on image gray level distribution and gray scale average value. In order to improve the recognition effects of the image, cropping, gray level transformation, contrast enhancement, median filtering and other preprocessing work were applied individually on the raw image of coal caving and rock caving acquired from mechanized top caving face, then gray histogram of image signal of coal and rock was abstracted and the gray scale mean were calculated. The results shows that (1) the range of gray s
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Peng, Na Xin, and Yu Qiang Chen. "Improved Self-Adaptive Image Histogram Equalization Algorithm." Advanced Materials Research 760-762 (September 2013): 1495–500. http://dx.doi.org/10.4028/www.scientific.net/amr.760-762.1495.

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Histogram equalization (HE) algorithm is wildly used method in image processing of contrast adjustment using images histogram. This method is useful in images with backgrounds and foreground that are both bright or both dark. But the performance of HE is not satisfactory to images with backgrounds and foregrounds that are both bright or both dark. To deal with the above problem, [ gives an improved histogram equalization algorithm named self-adaptive image histogram equalization (SIHE) algorithm. Its main idea is to extend the gray level of the image which firstly be processed by the classical
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Takemura, Kazuhisa, Iyuki Takasaki, and Yumi Iwamitsu. "Statistical Image Analysis of Psychological Projective Drawings." Journal of Advanced Computational Intelligence and Intelligent Informatics 9, no. 5 (2005): 453–60. http://dx.doi.org/10.20965/jaciii.2005.p0453.

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We propose statistical image analysis for psychological projective drawings to facilitate assessing the reliability of the projective drawing making the determination of its validity difficult. Standard analysis involves (1) drawing a picture, (2) scanning the drawing, (3) dividing the drawing, (4) analyzing the gray level histogram moment (GLHM), (5) applying spatial gray level dependence method (SGLDM), (6) applying the gray level difference method (GLDM) for the drawing, and (7) interpreting the drawing. To demonstrate the proposed procedure, we used the tree test (Baum test). Three adults
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Kabalyk, M. A. "Textural characteristics of subchondral bone in osteoarthritis." Kazan medical journal 97, no. 4 (2016): 518–23. http://dx.doi.org/10.17750/kmj2016-518.

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Aim. To assess the relationship between textural characteristics of the subchondral bone and standard X-ray data, to determine markers of subchondral bone remodeling in gonarthrosis.Methods. The studied group included 92 patients aged 66.1±10.5 years with I-IV grades osteoarthritis by the Kellgren, in the comparison group - 24 volunteers aged 29.6±5.96 years without clinical or radiological signs of gonarthrosis. Standard digital X-ray of the knee joint was performed. On the image, the area of interest was chosen, including a portion of the subchondral bone of 48±2×90±4 pixels of size. Accordi
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Vorobel, R. A., O. R. Berehulyak, I. B. Ivasenko, and T. S. Mandziy. "Modified method of image histogram hyperbolization." Information extraction and processing 2021, no. 49 (2021): 52–56. http://dx.doi.org/10.15407/vidbir2021.49.052.

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One of the methods to improve image quality, which consists in increasing the resolution of image details by contrast enhancement, is to hyperbolize the image histogram. Herewith this increase in local contrast is carried out indirectly. It is due to the nature of the change in the histogram of the transformed image. Usually the histogram of the input image is transformed so that it has a uniform distribution, which illustrates the same contribution of pixels gray level to the image structure. However, there is a method that is based on modeling the human visual system, which is characterized
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Zhan, Yantong, and Guoying Zhang. "An Improved OTSU Algorithm Using Histogram Accumulation Moment for Ore Segmentation." Symmetry 11, no. 3 (2019): 431. http://dx.doi.org/10.3390/sym11030431.

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When using image processing technology to analyze mineral particle size in complex scenes, it is difficult to separate the objects from the background with traditional algorithms. This paper proposes an ore image segmentation algorithm based on a histogram accumulation moment, which is applied to multi-scenario ore object location and recognition. Firstly, the multi-scale Retinex color restoration algorithm is used to improve the contrast in the dark region and eliminates the shadows generated by the stacked adhesion ores. Then, the zero-order and first-order cumulative moments close to the se
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Maeda, Kazuo, Mariko Serizawa, and Nobuhiro Yamamoto. "Ultrasound tissue characterization with the gray level histogram width of the B-mode." Ultrasound Review of Obstetrics and Gynecology 5, no. 2 (2005): 92–95. http://dx.doi.org/10.3109/14722240500190483.

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Oliveira, Débora M. N. M., Fabiano S. Costa, and Aurea Wischral. "BI-RADS classification and gray level histogram of malignant mammary tumors in bitches." Pesquisa Veterinária Brasileira 38, no. 10 (2018): 1942–48. http://dx.doi.org/10.1590/1678-5150-pvb-5220.

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ABSTRACT: Mammary tumor is the most frequent among the tumors that affect canine females, with relevant importance in veterinary medicine. The objective of this study was to determine the image characteristics of mammary tumors in female dogs, and compare different ultrasonographic techniques for neoplastic evaluation. During the experiment, 30 bitches with presence of nodular lesion in the mammary gland were used. Initially females were submitted to clinical and laboratory evaluations, and subsequent to the ultrasound examination of the tumor mass, as well as abdominal ultrasound and thoracic
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Yang, Wei, Lulu Cai, and Fei Wu. "Image segmentation based on gray level and local relative entropy two dimensional histogram." PLOS ONE 15, no. 3 (2020): e0229651. http://dx.doi.org/10.1371/journal.pone.0229651.

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Maeda, K., M. Utsu, P. E. Kihaile, M. Serizawa, N. Yamamoto, and T. Ito. "Tissue characterization with gray level histogram width and assessment of fetal lung immaturity." Ultrasound in Obstetrics and Gynecology 18 (October 2001): F27. http://dx.doi.org/10.1046/j.1469-0705.2001.abs18-7.x.

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Kim, Taeho, Cheol-Hee Lee, and Yong-Tae Do. "Real-time soybean classification using histogram and valley size in gray-level profile." Journal of Korean Society for Imaging Science and Technology 22, no. 2 (2016): 38–49. http://dx.doi.org/10.14226/ksist.2016.22.02.07.

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Liyuan Li, Ran Gong, and Weinan Chen. "Gray level image thresholding based on fisher linear projection of two-dimensional histogram." Pattern Recognition 30, no. 5 (1997): 743–49. http://dx.doi.org/10.1016/s0031-3203(96)00100-8.

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Maeda, Kazuo, Mariko Serizawa, and Nobuhiro Yamamoto. "Ultrasound tissue characterization with the gray level histogram width of the B-mode." Ultrasound Review of Obstetrics & Gynecology 5, no. 2 (2005): 92–95. http://dx.doi.org/10.1080/14722240500190483.

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