Muhammad Attique Khan^1,2, Reham R. Mostafa³, Yu-Dong Zhang², Jamel Baili⁴, Majed Alhaisoni⁵, Usman Tariq⁶, Junaid Ali Khan¹, Ye Jin Kim⁷, Jaehyuk Cha^7,*

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3029-3047, 2023, DOI:10.32604/cmc.2023.038838

Abstract Manual diagnosis of brain tumors using magnetic resonance images (MRI) is a hectic process and time-consuming. Also, it always requires an expert person for the diagnosis. Therefore, many computer-controlled methods for diagnosing and classifying brain tumors have been introduced in the literature. This paper proposes a novel multimodal brain tumor classification framework based on two-way deep learning feature extraction and a hybrid feature optimization algorithm. NasNet-Mobile, a pre-trained deep learning model, has been fine-tuned and two-way trained on original and enhanced MRI images. The haze-convolutional neural network (haze-CNN) approach is developed and employed on the original images for contrast enhancement.… More >

Goma Tshivetta Christian Fersein Jorvialom^1,2, Lord Amoah^1,2,*

Journal of Quantum Computing, Vol.4, No.3, pp. 183-197, 2022, DOI:10.32604/jqc.2022.039913

Abstract Prior versions of reversible data hiding with contrast enhancement (RDHCE) algorithms strongly focused on enhancing the contrast of grayscale images. However, RDHCE has recently witnessed a rise in contrast enhancement algorithms concentrating on color images. This paper implies a method for color images that uses the RGB (red, green, and blue) color model and is based on bi-histogram shifting and image adjustment. Bi-histogram shifting is used to embed data and image adjustment to achieve contrast enhancement by adjusting the images resulting from each channel of the color images before combining them to generate the final enhanced image. Images are first… More >

Samra Rehman¹, Muhammad Attique Khan¹, Majed Alhaisoni², Ammar Armghan³, Fayadh Alenezi³, Abdullah Alqahtani⁴, Khean Vesal⁵, Yunyoung Nam^5,*

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1179-1194, 2023, DOI:10.32604/cmc.2023.035324

Abstract Manual inspection of fruit diseases is a time-consuming and costly because it is based on naked-eye observation. The authors present computer vision techniques for detecting and classifying fruit leaf diseases. Examples of computer vision techniques are preprocessing original images for visualization of infected regions, feature extraction from raw or segmented images, feature fusion, feature selection, and classification. The following are the major challenges identified by researchers in the literature: (i) low-contrast infected regions extract irrelevant and redundant information, which misleads classification accuracy; (ii) irrelevant and redundant information may increase computational time and reduce the designed model’s accuracy. This paper proposed… More >

S. Priyadarsini^1,*, Carlos Andrés Tavera Romero², Abolfazl Mehbodniya³, P. Vidya Sagar⁴, Sudhakar Sengan⁵

Computer Systems Science and Engineering, Vol.43, No.3, pp. 1057-1068, 2022, DOI:10.32604/csse.2022.024788

Abstract In the recent days, the segmentation of Liver Tumor (LT) has been demanding and challenging. The process of segmenting the liver and accurately spotting the tumor is demanding due to the diversity of shape, texture, and intensity of the liver image. The intensity similarities of the neighboring organs of the liver create difficulties during liver segmentation. The manual segmentation does not provide an accurate segmentation because the results provided by different medical experts can vary. Also, this manual technique requires a large number of image slices and time for segmentation. To solve these issues, the Fully Automatic Segmentation (FAS) technique… More >

B. Priestly Shan¹, O. Jeba Shiney¹, Sharzeel Saleem², V. Rajinikanth³, Atef Zaguia⁴, Dilbag Singh^5,*

CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 2749-2763, 2022, DOI:10.32604/cmc.2022.023057

Abstract Low contrast of Magnetic Resonance (MR) images limits the visibility of subtle structures and adversely affects the outcome of both subjective and automated diagnosis. State-of-the-art contrast boosting techniques intolerably alter inherent features of MR images. Drastic changes in brightness features, induced by post-processing are not appreciated in medical imaging as the grey level values have certain diagnostic meanings. To overcome these issues this paper proposes an algorithm that enhance the contrast of MR images while preserving the underlying features as well. This method termed as Power-law and Logarithmic Modification-based Histogram Equalization (PLMHE) partitions the histogram of the image into two… More >

Balakrishnan Natarajan^1,*, Pushpalatha Krishnan²

Computer Systems Science and Engineering, Vol.42, No.2, pp. 423-438, 2022, DOI:10.32604/csse.2022.022376

Abstract In existing methods for segmented images, either edge point extraction or preservation of edges, compromising contrast images is so sensitive to noise. The Degeneration Threshold Image Detection (DTID) framework has been proposed to improve the contrast of edge filtered images. Initially, DTID uses a Rapid Bilateral Filtering process for filtering edges of contrast images. This filter decomposes input images into base layers in the DTID framework. With minimal filtering time, Rapid Bilateral Filtering handles high dynamic contrast images for smoothening edge preservation. In the DTID framework, Rapid Bilateral Filtering with Shift-Invariant Base Pass Domain Filter is insensitive to noise. This… More >

Zhe Yu¹, Bangyong Sun^1,3,*, Di Liu², Vincent Whannou de Dravo¹, Margarita Khokhlova⁴, Siyuan Wu³

Journal of Renewable Materials, Vol.10, No.5, pp. 1381-1395, 2022, DOI:10.32604/jrm.2022.018262

Abstract In this paper, we propose a neoteric and high-efficiency single image dehazing algorithm via contrast enhancement which is called STRASS (Spatio-Temporal Retinex-Inspired by an Averaging of Stochastic Samples) dehazing, it is realized by constructing an efficient high-pass filter to process haze images and taking the influence of human vision system into account in image dehazing principles. The novel high-pass filter works by getting each pixel using RSR and computes the average of the samples. Then the low-pass filter resulting from the minimum envelope in STRESS framework has been replaced by the average of the samples. The final dehazed image is… More >

Wan Azani Mustafa^1,2,*, Haniza Yazid³, Ahmed Alkhayyat⁴, Mohd Aminudin Jamlos³, Hasliza A. Rahim³

CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3549-3564, 2022, DOI:10.32604/cmc.2022.019801

Abstract Background: Contrast enhancement plays an important role in the image processing field. Contrast correction has performed an adjustment on the darkness or brightness of the input image and increases the quality of the image. Objective: This paper proposed a novel method based on statistical data from the local mean and local standard deviation. Method: The proposed method modifies the mean and standard deviation of a neighbourhood at each pixel and divides it into three categories: background, foreground, and problematic (contrast & luminosity) region. Experimental results from both visual and objective aspects show that the proposed method can normalize the contrast… More >

Naglaa F. Soliman^1,2, Naglaa S. Ali², Mahmoud I. Aly^2,3, Abeer D. Algarni^1,*, Walid El-Shafai⁴, Fathi E. Abd El-Samie^1,4

CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 1315-1334, 2022, DOI:10.32604/cmc.2022.017001

Abstract Breast cancer is the most common type of cancer, and it is the reason for cancer death toll in women in recent years. Early diagnosis is essential to handle breast cancer patients for treatment at the right time. Screening with mammography is the preferred examination for breast cancer, as it is available worldwide and inexpensive. Computer-Aided Detection (CAD) systems are used to analyze medical images to detect breast cancer, early. The death rate of cancer patients has decreased by detecting tumors early and having appropriate treatment after operations. Processing of mammogram images has four main steps: pre-processing, segmentation of the… More >

Ziqing Yan^1,2, Pengpeng Yang^1,2, Rongrong Ni^1,2,*, Yao Zhao^1,2, Hairong Qi³

CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3205-3216, 2021, DOI:10.32604/cmc.2021.020324

Abstract As one of the most popular digital image manipulations, contrast enhancement (CE) is frequently applied to improve the visual quality of the forged images and conceal traces of forgery, therefore it can provide evidence of tampering when verifying the authenticity of digital images. Contrast enhancement forensics techniques have always drawn significant attention for image forensics community, although most approaches have obtained effective detection results, existing CE forensic methods exhibit poor performance when detecting enhanced images stored in the JPEG format. The detection of forgery on contrast adjustments in the presence of JPEG post processing is still a challenging task. In… More >