Image enhancement with the preservation of brightness and structures by employing contrast limited dynamic quadri-histogram equalization

Huang, Zhenghua; Wang, Zhicheng; Zhang, Jing; Li, Qian; Shi, Yu

doi:10.1016/j.ijleo.2020.165877

Cited by 67 publications

(36 citation statements)

References 45 publications

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“…The small receptive fields which lead to an accurate distinction between distinct regions are utilized in this study for detecting key features. The convolutional layer can detect stimuli over a specific region with a small receptive field, resulting in more accurate detection of small changes in the tumor boundary [59,60].…”

Section: Proposed Convolutional Neural Network Architecturementioning

confidence: 99%

Nerve optic segmentation in CT images using a deep learning model and a texture descriptor

Ranjbarzadeh

Dorosti

Ghoushchi

et al. 2022

Complex Intell. Syst.

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The increased intracranial pressure (ICP) can be described as an increase in pressure around the brain and can lead to serious health problems. The assessment of ultrasound images is commonly conducted by skilled experts which is a time-consuming approach, but advanced computer-aided diagnosis (CAD) systems can assist the physician to decrease the time of ICP diagnosis. The accurate detection of the nerve optic regions, with drawing a precise slope line behind the eyeball and calculating the diameter of nerve optic, are the main aims of this research. First, the Fuzzy C-mean (FCM) clustering is employed for segmenting the input CT screening images into the different parts. Second, a histogram equalization approach is used for region-based image quality enhancement. Then, the Local Directional Number method (LDN) is used for representing some key information in a new image. Finally, a cascade Convolutional Neural Network (CNN) is employed for nerve optic segmentation by two distinct input images. Comprehensive experiments on the CT screening dataset [The Cancer Imaging Archive (TCIA)] consisting of 1600 images show the competitive results of inaccurate extraction of the brain features. Also, the indexes such as Dice, Specificity, and Precision for the proposed approach are reported 87.7%, 91.3%, and 90.1%, respectively. The final classification results show that the proposed approach effectively and accurately detects the nerve optic and its diameter in comparison with the other methods. Therefore, this method can be used for early diagnose of ICP and preventing the occurrence of serious health problems in patients.

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Section: Proposed Convolutional Neural Network Architecturementioning

confidence: 99%

Nerve optic segmentation in CT images using a deep learning model and a texture descriptor

Ranjbarzadeh

Dorosti

Ghoushchi

et al. 2022

Complex Intell. Syst.

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“…Optimization strategies could be then used to reduce these side effects [28]. For instance, a HEvariant approach [29] finds an optimal threshold gray level value that separates the histogram into four sub-histogram. Histogram clipping is then applied to adjust the threshold according to the distribution of the original histogram.…”

Section: Related Workmentioning

confidence: 99%

Cross-Modality Guided Contrast Enhancement for Improved Liver Tumor Image Segmentation

Naseem¹,

Khan²,

Satpute

et al. 2021

IEEE Access

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Tumor segmentation in Computed Tomography (CT) images is a crucial step in image-guided surgery. However, low-contrast CT images impede the performance of subsequent segmentation tasks. Contrast enhancement is then used as a preprocessing step to highlight the relevant structures, thus facilitating not only medical diagnosis but also image segmentation with higher accuracy. In this paper, we propose a goal-oriented contrast enhancement method to improve tumor segmentation performance. The proposed method is based on two concepts, namely guided image enhancement and image quality control through an optimization scheme. The proposed OPTimized Guided Contrast Enhancement (OPTGCE) scheme exploits both contextual information from the guidance image and structural information from the input image in a two-step process. The first step consists of applying a two-dimensional histogram specification exploiting contextual information in the corresponding guidance image, i.e. Magnetic Resonance Image (MRI). In the second step, an optimization scheme using a structural similarity measure to preserve the structural information of the original image is performed. To the best of our knowledge, this kind of contrast enhancement optimization scheme using cross-modal guidance is proposed for the first time in the medical imaging context. The experimental results obtained on real data demonstrate the effectiveness of the method in terms of enhancement and segmentation quality in comparison to some state-of-the-art methods based on the histogram.

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“…The TIE-Net is a modified network in which to address thermal imaging at night and then optimize the TIR image to obtain more complex features of a person. TIR image is optimized through passing into TIE-Net, which uses the Histogram Equalization method [45] to adjust the brightness. The optimized strategy is used in convolution layers of the TIE-Net.…”

Section: Network Architecturementioning

confidence: 99%

Real-time human detection in thermal infrared imaging at night using enhanced Tiny-yolov3 network

Manssor

Sun

Abdalmajed

et al. 2021

J Real-Time Image Proc

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Human detection is a technology that detects human shapes in the image and ignores everything else. However, modern person detectors have some inefficiencies in detecting pedestrians during video surveillance at night, and the accuracy rate is still insufficient. Therefore, this paper aims to increase the accuracy rate for automatic human detection at night from thermal infrared (TIR) images and real-time video sequences. For this purpose, a new architecture is proposed to enhance the backbone of the Tiny-yolov3 network. The enhanced network used the YOLOv3 algorithm's tasks with the K-means clustering method to extract more complex features of a person. This network was pre-trained on the MS. COCO dataset to obtain the initial weights. Through the comparison with other related methods showed that the experimental results have achieved the significantly improved performance of human detection from thermal imaging in terms of accuracy, speed, and detection time. The method has achieved a high accuracy rate (90%) compared with the TF-YOLOv3 (88%) trained on the DHU Night Dataset. Although the method has achieved an accuracy rate equal to the YOLOv3-Human (90%), the detection time (4.88 ms) is less, Furthermore, the method has a higher accuracy rate (49.8%) than the YOLO (29.36%) and TF-YOLOv3 (29.8%) with lower detection time (8 ms) on the FLIR Dataset. In addition, the model has achieved a good TP detection for multiple small size of person. By improving the performance of human detection in thermal imaging at night, the method will be able to detect intruders in the night surveillance system.

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Image enhancement with the preservation of brightness and structures by employing contrast limited dynamic quadri-histogram equalization

Cited by 67 publications

References 45 publications

Nerve optic segmentation in CT images using a deep learning model and a texture descriptor

Nerve optic segmentation in CT images using a deep learning model and a texture descriptor

Cross-Modality Guided Contrast Enhancement for Improved Liver Tumor Image Segmentation

Real-time human detection in thermal infrared imaging at night using enhanced Tiny-yolov3 network

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