Efficient Medical Image Enhancement using CLAHE Enhancement and Wavelet Fusion
15 Jun 2017-International Journal of Computer Applications (Foundation of Computer Science (FCS), NY, USA)-Vol. 167, Iss: 5, pp 1-5
TL;DR: A combination of the contrast limited adaptive histogram equalization (CLAHE) method and the wavelet based Fusion techniques are used and it is found that based on adaptive Fusion the visual content of the medical images are efficiently improved under all kind of capturing environments.
Abstract: Medical image processing is a challenging field of research since the captured images suffers from the noise and poor contrast. The efficiency of the medical image processing depends on the quality of the captured medical images. Major factors for the low contrast medical images are age of capturing equipments, poor illumination conditions and inexperience of medical staff. Thus, contrast enhancement methods are used for improving the contrast of medical images before being used. In this paper an combination of the contrast limited adaptive histogram equalization (CLAHE) method and the wavelet based Fusion techniques are used for designing the efficient medical image enhancement method. Method is capable of adapting the Fusion rules adaptively for best enhancement results. First CLAHE image enhancement is used for improving the contrast of the medical images. then in second stage 2D Discrete wavelet transformation based adaptive image fusion is used for fusing the original and CLAHE output images. For testing the performance SNR and entropy are calculated and used as parameters. It is found that based on adaptive Fusion the visual content of the medical images are efficiently improved under all kind of capturing environments.
Citations
More filters
TL;DR: This research work analyses the role of image fusion in an improved brain tumour classification model, and this novel fusion-based cancer classification model can be used for personalized medicine more effectively.
Abstract: Image fusion can be performed on images either in spatial domain or frequency domain methods. Frequency domain methods will be most preferred because these methods can improve the quality of edges in an image. In image fusion, the resultant fused images will be more informative than individual input images, thus more suitable for classification problems. Artificial intelligence (AI) algorithms play a significant role in improving patient's treatment in the health care industry and thus improving personalized medicine. This research work analyses the role of image fusion in an improved brain tumour classification model, and this novel fusion-based cancer classification model can be used for personalized medicine more effectively. Image fusion can improve the quality of resultant images and thus improve the result of classifiers. Instead of using individual input images, the high-quality fused images will provide better classification results. Initially, the contrast limited adaptive histogram equalization technique preprocess input images such as MRI and SPECT images. Benign and malignant class brain tumor images are applied with discrete cosine transform-based fusion method to obtain fused images. AI algorithms such as support vector machine classifier, KNN classifier, and decision tree classifiers are tested with features obtained from fused images and compared with the result obtained from individual input images. Performances of classifiers are measured using the parameters accuracy, precision, recall, specificity, and F1 score. SVM classifier provided the maximum accuracy of 96.8%, precision of 95%, recall of 94%, specificity of 93%, F1 score of 91%, and performed better than KNN and decision tree classifiers when extracted features from fused images are used. The proposed method results are compared with existing methods and provide satisfactory results.
21 citations
TL;DR: The proposed technique can aid clinicians in glaucoma detection at an early stage with the highest accuracy of 94.3% using LPQ coupled with PCA for right eye optic disc images with AdaBoost classifier.
15 citations
21 Sep 2020
TL;DR: A segmentation independent two-stage preprocessing based technique is proposed which can effectively extract DR pathognomonic signs; both bright and red lesions, and blood vessels from the eye fundus image.
Abstract: Diabetic retinopathy (DR) is one of the severe eye conditions due to diabetes complication which can lead to vision loss if left untreated. In this paper, a computationally simple, yet very effective, DR detection method is proposed. First, a segmentation independent two-stage preprocessing based technique is proposed which can effectively extract DR pathognomonic signs; both bright and red lesions, and blood vessels from the eye fundus image. Then, the performance of Local Binary Patterns (LBP), Local Ternary Patterns (LTP), Dense Scale-Invariant Feature Transform (DSIFT) and Histogram of Oriented Gradients (HOG) as a feature descriptor for fundus images, is thoroughly analyzed. SVM kernel-based classifiers are trained and tested, using a 5-fold cross-validation scheme, on both newly acquired fundus image database from the local hospital and combined database created from the open-sourced available databases. The classification accuracy of 96.6% with 0.964 sensitivity and 0.969 specificity is achieved using a Cubic SVM classifier with LBP and LTP fused features for the local database. More importantly, in out-of-sample testing on the combined database, the model gives an accuracy of 95.21% with a sensitivity of 0.970 and specificity of 0.932. This indicates the proposed model is very well-fitted and generalized which is further corroborated by the presented train-test curves.
11 citations
01 Nov 2018
TL;DR: This study proposes the use of a rotation invariant algorithm like ORB for a bimodal approach in the vein recognition from the palm and dorsal vein patterns of the hand.
Abstract: The use of biometrics provides a more reliable security layer in identification and user authentication in many fields of commercial and institutional transactions. Vascular vein patterns from the hand have recently been explored as another approach to the biometric modality because these innate patterns are constant, remains distinct lifelong; relatively stable, cannot be forged, tampered nor copied. This study proposes the use of a rotation invariant algorithm like ORB for a bimodal approach in the vein recognition from the palm and dorsal vein patterns of the hand. Using near-infrared LEDs, Raspberry Pi with NOIR camera module, a portable, real time hand vein pattern recognition device was developed. Image pre-processing, feature extraction, feature matching, database of user information and GUI are implemented in Raspberry Pi, Python and OpenCV Libraries. ORB was used for generating feature descriptors and BruteForce Matcher for feature matching. Match scores generated by the classifier from dorsal and palm vein were combined using sum-rule in score level fusion to generate the final recognition results. After experimental tests conducted, system performance resulted to 95.00% accuracy level and overall response time of 2. 76secs. The developed architecture can be integrated with other systems like attendance monitoring, access control, identity authentication for financial transactions, forensic investigation, and fraud detection.
10 citations
Cites methods from "Efficient Medical Image Enhancement..."
...CLAHE method is used as described in [16], in which the image is divided into small regions called “tiles” or 8x8 small blocks, contrast transform is applied to each block; limiting function is set to reach a threshold value and bilinear interpolation is applied at each histogram blocks....
[...]
TL;DR: This study proposes methods to reduce the model variance of a rectal cancer segmentation network by adding a rectum segmentation task and performing data augmentation and proposes a method to perform a bias-variance analysis within an arbitrary region-of-interest (ROI) of a segmentationNetwork to assess the efficacy of these approaches in reducing model variance.
Abstract: In preoperative imaging, the demarcation of rectal cancer with magnetic resonance images provides an important basis for cancer staging and treatment planning. Recently, deep learning has greatly improved the state-of-the-art method in automatic segmentation. However, limitations in data availability in the medical field can cause large variance and consequent overfitting to medical image segmentation networks. In this study, we propose methods to reduce the model variance of a rectal cancer segmentation network by adding a rectum segmentation task and performing data augmentation; the geometric correlation between the rectum and rectal cancer motivated the former approach. Moreover, we propose a method to perform a bias-variance analysis within an arbitrary region-of-interest (ROI) of a segmentation network, which we applied to assess the efficacy of our approaches in reducing model variance. As a result, adding a rectum segmentation task reduced the model variance of the rectal cancer segmentation network within tumor regions by a factor of 0.90; data augmentation further reduced the variance by a factor of 0.89. These approaches also reduced the training duration by a factor of 0.96 and a further factor of 0.78, respectively. Our approaches will improve the quality of rectal cancer staging by increasing the accuracy of its automatic demarcation and by providing rectum boundary information since rectal cancer staging requires the demarcation of both rectum and rectal cancer. Besides such clinical benefits, our method also enables segmentation networks to be assessed with bias-variance analysis within an arbitrary ROI, such as a cancerous region.
10 citations
Cites methods from "Efficient Medical Image Enhancement..."
...We also applied contrast-limited adaptive histogram equalization to enhance the contrast as well as to reduce the illumination effect [25]–[27]....
[...]
References
More filters
2,671 citations
TL;DR: The selected CLAHE settings should be tested in the clinic with digital mammograms to determine whether detection of spiculations associated with masses detected at mammography can be improved.
Abstract: The purpose of this project was to determine whether Contrast Limited Adaptive Histogram Equalization (CLAHE) improves detection of simulated spiculations in dense mammograms Lines simulating the appearance of spiculations, a common marker of malignancy when visualized with masses, were embedded in dense mammograms digitized at 50 micron pixels, 12 bits deep Film images with no CLAHE applied were compared to film images with nine different combinations of clip levels and region sizes applied A simulated spiculation was embedded in a background of dense breast tissue, with the orientation of the spiculation varied The key variables involved in each trial included the orientation of the spiculation, contrast level of the spiculation and the CLAHE settings applied to the image Combining the 10 CLAHE conditions, 4 contrast levels and 4 orientations gave 160 combinations The trials were constructed by pairing 160 combinations of key variables with 40 backgrounds Twenty student observers were asked to detect the orientation of the spiculation in the image There was a statistically significant improvement in detection performance for spiculations with CLAHE over unenhanced images when the region size was set at 32 with a clip level of 2, and when the region size was set at 32 with a clip level of 4 The selected CLAHE settings should be tested in the clinic with digital mammograms to determine whether detection of spiculations associated with masses detected at mammography can be improved
554 citations
30 May 2013
TL;DR: Experimental results show that the proposed approach significantly improves the visual quality of underwater images by enhancing contrast, as well as reducing noise and artifacts.
Abstract: Within the last decades, improving the quality of an underwater image has received considerable attention due to poor visibility of the image which is caused by physical properties of the water medium. This paper presents a new method called mixture Contrast Limited Adaptive Histogram Equalization (CLAHE) color models that specifically developed for underwater image enhancement. The method operates CLAHE on RGB and HSV color models and both results are combined together using Euclidean norm. The underwater images used in this study were taken from Redang Island and Bidong Island in Terengganu, Malaysia. Experimental results show that the proposed approach significantly improves the visual quality of underwater images by enhancing contrast, as well as reducing noise and artifacts.
285 citations
TL;DR: The proposed technique, computationally more efficient than the spatial domain based method, is found to provide better enhancement compared to other compressed domain based approaches.
Abstract: This paper presents a new technique for color enhancement in the compressed domain. The proposed technique is simple but more effective than some of the existing techniques reported earlier. The novelty lies in this case in its treatment of the chromatic components, while previous techniques treated only the luminance component. The results of all previous techniques along with that of the proposed one are compared with respect to those obtained by applying a spatial domain color enhancement technique that appears to provide very good enhancement. The proposed technique, computationally more efficient than the spatial domain based method, is found to provide better enhancement compared to other compressed domain based approaches.
238 citations
"Efficient Medical Image Enhancement..." refers methods in this paper
...[1] Jayanta M., and Sanjit K. Mitra, “Enhancement of Color Images by Scaling the DCT Coefficients”, IEEE Transactions on Image Processing, Vol. 17, No. 10, pp. 1783-1794, 2008 [2] Prateek S. Sengar, Tarun K. Rawat, Harish Parthasarathy, “Color Image Enhancement by Scaling the Discrete Wavelet Transform Coefficients”, IEEE International Conference on Microelectronics, Communication and Renewable Energy (ICMiCR), 2013 [3] Peng Geng, Xing Su, Tan Xu , Jianshu Liu “ Multi-modal Medical Image Fusion Based on the Multiwavelet and Non sub sampled Direction Filter Bank” International Journal of Signal Processing, Image Processing and Pattern Recognition Vol.8, No.11 pp.75-84 2015, [4] Nayera Nahvi, Deep Mittal, “Medical Image Fusion Using Discrete Wavelet Transform”, International Journal of Engineering Research and Applications Vol. 4, Issue 9( Version 5), pp.165-170 September 2014 [5] K. Zuiderveld, "Contrast Limited Adaptive Histogram Equalization", Graphics Gems IV, pp. 474-485 [6] S. Senthilkumar, S. Muttan, “Effective Multiresolute Computation to Remote Sensed Data Fusion” IEEE International Conference on Computational Intelligence and Multimedia Applications, 2007 [7] K. Kannan, S. Arumuga Perumal, K. Arulmozhi, “Area level fusion of Multi-focused Images using MultiStationary Wavelet Packet Transform “,International Journal of Computer Applications (0975 – 8887) Volume 2 – No.1, May 2010 [8] Anjali Malviya, S. G. Bhirud, “Image Fusion of Digital Images”, International Journal of Recent Trends in Engineering, Vol. 2, No. 3, November 2009 [9] Susmitha Vekkot, and Pancham Shukla, “A Novel Architecture for Wavelet based Image Fusion” Journal of World Academy of Science, Engineering and Technology, 57, pp. 32-3, 2009 [10] Jun Kong, Kaiyuan Zheng, Jingbo Zhang, Xue Feng, “Multi-focus Image Fusion Using Spatial Frequency and Genetic Algorithm”,”, IJCSNS International Journal of Computer Science and Network Security, Vol..8, No. 2, pp 220-224, February 2008 [11] J Gao, Z. Liu and T. Ren, “A new image fusion scheme based on wavelet transform Proc....
[...]
...While most commonly used transform domain methods uses DWT [4] and DCT [1] for image enhancement Contrast Limited Adaptive Histogram Equalization (CLAHE) is widely used maximal entropy based spatial domain enhancement method [14] method is the most popular spatial domain enhancement method....
[...]
13 Jun 2013
TL;DR: This research uses Contrast Limited Adaptive Histogram Equalization (CLAHE) to enhance the color retinal image and proposes new enhancement method using CLAHE in G channel to improve the color Retinal image quality.
Abstract: Common method in image enhancement that's often use is histogram equalization, due to this method is simple and has low computation load. In this research, we use Contrast Limited Adaptive Histogram Equalization (CLAHE) to enhance the color retinal image. To reduce this noise effect in color retinal image due to the acquisition process, we need to enhance this image. Color retinal image has unique characteristic than other image, that is, this image has important in green (G) channel. Image enhancement has important contribution in ophthalmology. In this paper, we propose new enhancement method using CLAHE in G channel to improve the color retinal image quality. The enhancement process conduct in G channel is appropriate to enhance the color retinal image quality.
162 citations