Comparative Evaluation of Medical Thermal Image Enhancement Techniques for Breast Cancer Detection

Thermography is a potential medical imaging modality due to its capability in providing additional physiological information. Medical thermal images obtained from infrared thermography systems incorporate valuable temperature properties and profiles, which could indicate underlying abnormalities. The quality of thermal images is often degraded due to noise, which affects the measurement processes in medical imaging. Contrast stretching and image filtering techniques are normally adopted in medical image enhancement processes. In this study, a comparative evaluation of contrast stretching and image filtering on individual channels of true color thermal images was conducted. Their individual performances were quantitatively measured using mean square error (MSE) and peak signal to noise ratio (PSNR). The results obtained showed that contrast stretching altered the temperature profile of the original image while image filtering appeared to enhance the original image with no changes in its profile. Further measurement of both MSE and PSNR showed that the Wiener filtering method outperformed other filters with an average MSE value of 0.0045 and PSNR value of 78.739 dB. Various segmentation methods applied to both filtered and contrast stretched images proved that the filtering method is preferable for in-depth analysis

Comparative evaluation of medical thermal image enhancement techniques for breast cancer detection

Thermography is a potential medical imaging modality due to its capability in providing additional physiological information. Medical thermal images obtained from infrared thermography systems incorporate valuable temperature properties and profiles, which could indicate underlying abnormalities. The quality of thermal images is often degraded due to noise, which affects the measurement processes in medical imaging. Contrast stretching and image filtering techniques are normally adopted in medical image enhancement processes. In this study, a comparative evaluation of contrast stretching and image filtering on individual channels of true color thermal images was conducted. Their individual performances were quantitatively measured using mean square error (MSE) and peak signal to noise ratio (PSNR). The results obtained showed that contrast stretching altered the temperature profile of the original image while image filtering appeared to enhance the original image with no changes in its profile. Further measurement of both MSE and PSNR showed that the Wiener filtering method outperformed other filters with an average MSE value of 0.0045 and PSNR value of 78.739 dB. Various segmentation methods applied to both filtered and contrast stretched images proved that the filtering method is preferable for in-depth analysis

An Investigation into the application of digital camera created images and their preparation for newspaper lithographic reproduction without a reference analog reflection or transmission original

Looking at the shoes that digital photography has a role in filling we can quickly see performance issues that will undoubtedly affect our final printed reproduction. The resolution provided by a digital image is directly correlated to the CCD size, quality and any associated post-imaging processes provided by cam era manufacturers but is only one of quality-limiting factors involved in digital photography. Silicon-based CCD\u27s are monochrome in nature. \u27 Having the inability to decipher the varying degrees of red, green and blue light presented to the pixels it is necessary to account for RGB light by introducing a color-filtering method. The color-filtering method prevalent in the cameras to be tested are referred to as integral color filter arrays (CFA). 2 Integral color filter arrays performs color filtering on the chip with each individual pixel hosting a specific filter color (red, green or blue). This allows for the deciphering of the various light wavelengths presented in a scene to be translated by the CCD into digitized values of color, but because each pixel can only represent one filter color this results in problems including; the loss of information leading to reduced effective resolution and increased sampling (quantizing) artifacts. 3 Such color-gathering techniques and various inherent CCD issues account for problems that must be addressed and minimized during post-image processing prepress steps. The following endeavor is to evaluate three types of digital cameras (Minolta RD-175, Fuji 505a, AP NC2000e/Canon DCS EOS3) which can meet the requirements of a photojournalist then identifying the various issues that are inherent to each camera, post processing prepress solutions will be sought through the use of Adobe Photoshop. By evaluating the cameras via tests that provide information about resolution, dynamic range, color gamut reproduction abilities and image-to-noise relationships it was possible to assess what cam era shortcomings must be addressed during post-image processing. The shortcomings were then individually assessed and, utilizing prepress skills post-processing procedures, were identified to address the specific inherent shortcomings. Using SNAP (specifications for non-heat advertising printing) specifications, a representative set of images were printed and analyzed. The results from this analysis presents camera performance issues prior to post-image processing optimization and after post-image processing optimization. It will illustrate the initial shortcomings and how well these shortcomings can be de-emphasized in Adobe Photoshop. The printing of test images to SNAP specifications also illustrates if there is any loss of quali ty due to the reproduction on newsprint. Based on the test performed it was established that each usable camera ISO has its own specific set of characteristics that effect visual resolution, color gamut, usable range and noise. The method the man ufacturer uses to acquire its images, including CCD hardware, camera firmware and pre-acquire pro cessing, also affect visual resolution, color gamut, usable range, noise and aliasing. Photographic metering techniques and photographer criteria for ISO selection can assist in main taining the highest level of exposure quality capable for each camera. When the highest level of image quality is achieved with the use of photographic techniques, the highest level of visual resolution, color gamut, usable range and the least noise can be rendered for each camera image. Knowledge of Adobe Photoshop and offset printing principles, such as memory colors, wanted and unwanted colors, are valuable in enhancing the digital camera\u27s limited color gamut. The nature of the newspaper printing process produces a small color gamut, therefore, the limited gamut inherent in the digital cameras is of less concern than if the digital images were printed using a larger color gamut capa ble four-color process. The identification of each camera\u27s tendencies does allow for a greater understanding of applic able procedures within Adobe Photoshop which can reduce and or alleviate the tendencies

Colour image denoising by eigenvector analysis of neighbourhood colour samples

[EN] Colour image smoothing is a challenging task because it is necessary to appropriately distinguish between noise and original structures, and to smooth noise conveniently. In addition, this processing must take into account the correlation among the image colour channels. In this paper, we introduce a novel colour image denoising method where each image pixel is processed according to an eigenvector analysis of a data matrix built from the pixel neighbourhood colour values. The aim of this eigenvector analysis is threefold: (i) to manage the local correlation among the colour image channels, (ii) to distinguish between flat and edge/textured regions and (iii) to determine the amount of needed smoothing. Comparisons with classical and recent methods show that the proposed approach is competitive and able to provide significative improvements.Latorre-Carmona, P.; Miñana, J.; Morillas, S. (2020). Colour image denoising by eigenvector analysis of neighbourhood colour samples. Signal Image and Video Processing. 14(3):483-490. https://doi.org/10.1007/s11760-019-01575-5S483490143Plataniotis, K.N., Venetsanopoulos, A.N.: Color Image Processing and Applications. Springer, Berlin (2000)Lukac, R., Smolka, B., Martin, K., Plataniotis, K.N., Venetsanopoulos, A.N.: Vector Filtering for Color Imaging. IEEE Signal Processing Magazine, Special Issue on Color Image Processing 22, 74–86 (2005)Lukac, R., Plataniotis, K.N.: A taxonomy of color image filtering and enhancement solutions. In: Hawkes, P.W. (ed.) Advances in Imaging and Electron Physics, vol. 140, pp. 187–264. Elsevier Acedemic Press, Amsterdam (2006)Buades, A., Coll, B., Morel, J.M.: Nonlocal image and movie denoising. Int. J. Comput. Vis. 76, 123–139 (2008)Tomasi, C., Manduchi, R.: Bilateral filter for gray and color images. In: Proceedings of IEEE International Conference Computer Vision, pp. 839–846 (1998)Elad, M.: On the origin of bilateral filter and ways to improve it. IEEE Trans. Image Process. 11, 1141–1151 (2002)Kao, W.C., Chen, Y.J.: Multistage bilateral noise filtering and edge detection for color image enhancement. IEEE Trans. Consum. Electron. 51, 1346–1351 (2005)Garnett, R., Huegerich, T., Chui, C., He, W.: A universal noise removal algorithm with an impulse detector. IEEE Trans. Image Process. 14, 1747–1754 (2005)Morillas, S., Gregori, V., Sapena, A.: Fuzzy Bilateral Filtering for color images. Lecture Notes Comput. Sci. 4141, 138–145 (2006)Zhang, B., Allenbach, J.P.: Adaptive bilateral filter for sharpness enhancement and noise removal. IEEE Trans. Image Process. 17, 664–678 (2008)Kenney, C., Deng, Y., Manjunath, B.S., Hewer, G.: Peer group image enhancement. IEEE Trans. Image Process. 10, 326–334 (2001)Morillas, S., Gregori, V., Hervás, A.: Fuzzy peer groups for reducing mixed Gaussian-impulse noise from color images. IEEE Trans. Image Process. 18, 1452–1466 (2009)Plataniotis, K.N., Androutsos, D., Venetsanopoulos, A.N.: Adaptive fuzzy systems for multichannel signal processing. Proc. IEEE 87, 1601–1622 (1999)Schulte, S., De Witte, V., Kerre, E.E.: A fuzzy noise reduction method for colour images. IEEE Trans. Image Process. 16, 1425–1436 (2007)Shen, Y., Barner, K.: Fuzzy vector median-based surface smoothing. IEEE Trans. Vis. Comput. Graph. 10, 252–265 (2004)Lukac, R., Plataniotis, K.N., Smolka, B., Venetsanopoulos, A.N.: cDNA microarray image processing using fuzzy vector filtering framework. Fuzzy Sets Syst. 152, 17–35 (2005)Smolka, B.: On the new robust algorithm of noise reduction in color images. Comput. Graph. 27, 503–513 (2003)Van de Ville, D., Nachtegael, M., Van der Weken, D., Philips, W., Lemahieu, I., Kerre, E.E.: Noise reduction by fuzzy image filtering. IEEE Trans. Fuzzy Syst. 11, 429–436 (2003)Schulte, S., De Witte, V., Nachtegael, M., Van der Weken, D., Kerre, E.E.: Histogram-based fuzzy colour filter for image restoration. Image Vis. Comput. 25, 1377–1390 (2007)Nachtegael, M., Schulte, S., Van der Weken, D., De Witte, V., Kerre, E.E.: Gaussian noise reduction in grayscale images. Int. J. Intell. Syst. Technol. Appl. 1, 211–233 (2006)Schulte, S., De Witte, V., Nachtegael, M., Mélange, T., Kerre, E.E.: A new fuzzy additive noise reduction method. Lecture Notes Comput. Sci. 4633, 12–23 (2007)Morillas, S., Schulte, S., Mélange, T., Kerre, E.E., Gregori, V.: A soft-switching approach to improve visual quality of colour image smoothing filters. In: Proceedings of Advanced Concepts for Intelligent Vision Systems ACIVS07, Lecture Notes in Computer Science, vol. 4678, pp. 254–261 (2007)Lucchese, L., Mitra, S.K.: A new class of chromatic filters for color image processing: theory and applications. IEEE Trans. Image Process. 13, 534–548 (2004)Lee, J.A., Geets, X., Grégoire, V., Bol, A.: Edge-preserving filtering of images with low photon counts. IEEE Trans. Pattern Anal. Mach. Intell. 30, 1014–1027 (2008)Russo, F.: Technique for image denoising based on adaptive piecewise linear filters and automatic parameter tuning. IEEE Trans. Instrum. Meas. 55, 1362–1367 (2006)Shao, M., Barner, K.E.: Optimization of partition-based weighted sum filters and their application to image denoising. IEEE Trans. Image Process. 15, 1900–1915 (2006)Ma, Z., Wu, H.R., Feng, D.: Partition based vector filtering technique for suppression of noise in digital color images. IEEE Trans. Image Process. 15, 2324–2342 (2006)Ma, Z., Wu, H.R., Feng, D.: Fuzzy vector partition filtering technique for color image restoration. Comput. Vis. Image Underst. 107, 26–37 (2007)Perona, P., Malik, J.: Scale-space and edge detection using anisotropic diffusion. IEEE Trans. Pattern Anal. Mach. Intell. 12, 629–639 (1990)Sroubek, F., Flusser, J.: Multichannel blind iterative image restoration. IEEE Trans. Image Process. 12, 1094–1106 (2003)Hu, J., Wang, Y., Shen, Y.: Noise reduction and edge detection via kernel anisotropic diffusion. Pattern Recognit. Lett. 29, 1496–1503 (2008)Li, X.: On modeling interchannel dependency for color image denoising. Int. J. Imaging Syst. Technol., Special issue on applied color image processing 17, 163–173 (2007)Keren, D., Gotlib, A.: Denoising color images using regularization and correlation terms. J. Vis. Commun. Image Represent. 9, 352–365 (1998)Lezoray, O., Elmoataz, A., Bougleux, S.: Graph regularization for color image processing. Comput. Vis. Image Underst. 107, 38–55 (2007)Elmoataz, A., Lezoray, O., Bougleux, S.: Nonlocal discrete regularization on weighted graphs: a framework for image and manifold processing. IEEE Trans. Image Process. 17, 1047–1060 (2008)Blomgren, P., Chan, T.: Color TV: total variation methods for restoration of vector-valued images. IEEE Trans. Image Process. 7, 304–309 (1998)Tschumperlé, D., Deriche, R.: Vector-valued image regularization with PDEs: a common framework from different applications. IEEE Trans. Pattern Anal. Mach. Intell. 27, 506–517 (2005)Plonka, G., Ma, J.: Nonlinear regularized reaction-diffusion filters for denoising of images with textures. IEEE Trans. Image Process. 17, 1283–1294 (2007)Melange, T., Zlokolica, V., Schulte, S., De Witte, V., Nachtegael, M., Pizurca, A., Kerre, E.E., Philips, W.: A new fuzzy motion and detail adaptive video filter. Lecture Notes Comput. Sci. 4678, 640–651 (2007)De Backer, S., Pizurica, A., Huysmans, B., Philips, W., Scheunders, P.: Denoising of multicomponent images using wavelet least-squares estimators. Image Vis. Comput. 26, 1038–1051 (2008)Dengwen, Z., Wengang, C.: Image denoising with an optimal threshold and neighboring window. Pattern Recognit. Lett. 29, 1694–1697 (2008)Schulte, S., Huysmans, B., Pizurica, A., Kerre, E.E., Philips, W.: A new fuzzy-based wavelet shrinkage image denoising technique. In: Proceedings of Advanced Concepts for Intelligent Vision Systems ACIVS06, Lecture Notes in Computer Science, vol. 4179, pp. 12–23 (2006)Pizurica, A., Philips, W.: Estimating the probability of the presence of a signal of interest in multiresolution single and multiband image denoising. IEEE Trans. Image Process. 15, 654–665 (2006)Scheunders, P.: Wavelet thresholding of multivalued images. IEEE Trans. Image Process. 13, 475–483 (2004)Sendur, L., Selesnick, I.W.: Bivariate shrinkage functions for wavelet-based denoising exploiting interscale dependency. IEEE Trans. Signal Process. 50, 2744–2756 (2002)Balster, E.J., Zheng, Y.F., Ewing, R.L.: Feature-based wavelet shrinkage algorithm for image denoising. IEEE Trans. Image Process. 14, 2024–2039 (2005)Miller, M., Kingsbury, N.: Image denoising using derotated complex wavelet coefficients. IEEE Trans. Image Process. 17, 1500–1511 (2008)Zhang, B., Fadili, J.M., Starck, J.L.: Wavelets, ridgelets, and curvelets for poisson noise removal. IEEE Trans. Image Process. 17, 1093–1108 (2008)Dabov, K., Foi, A., Katkovnik, V., Egiazarian, K.: Image denoising by sparse 3D transform-domain collaborative filtering. IEEE Trans. Image Process. 16, 2080–2095 (2007)Dabov, K., Foi, A., Katkovnik, V., Egiazarian, K.: Color image denoising via sparse 3D collaborative filtering with grouping constraint in luminance-chrominance space. In: Proceedings of the IEEE International Conference on Image Processing ICIP2007 , pp. 313–316 (2007)Hao, B.B., Li, M., Feng, X.C.: Wavelet iterative regularization for image restoration with varying scale parameter. Signal Process. Image Commun. 23, 433–441 (2008)Zhao, W., Pope, A.: Image restoration under significat additive noise. IEEE Signal Process. Lett. 14, 401–404 (2007)Gijbels, I., Lambert, A., Qiu, P.: Edge-preserving image denoising and estimation of discontinuous surfaces. IEEE Trans. Pattern Anal. Mach. Intell. 28, 1075–1087 (2006)Liu, C., Szeliski, R., Kang, S.B., Zitnik, C.L., Freeman, W.T.: Automatic estimation and removal of noise from a single image. IEEE Trans. Pattern Anal. Mach. Intell. 30, 299–314 (2008)Oja, E.: Principal components, minor components, and linear neural networks. Neural Netw. 5, 927–935 (1992)Takahashi, T.: Kurita, T.: Robust de-noising by kernel PCA. In: Proceedings of ICANN2002, Lecture Notes in Computer Science, vol. 2145, pp. 739–744 (2002)Park, H., Moon, Y.S.: Automatic denoising of 2D color face images using recursive PCA reconstruction. In: Proceedings of Advanced Concepts for Intelligent Vision Systems ACIVS06, Lecture Notes in Computer Science, vol. 4179, pp. 799–809 (2006)Teixeira, A.R., Tomé, A.M., Stadlthanner, K., Lang, E.W.: KPCA denoising and the pre-image problem revisited. Digital Signal Process. 18, 568–580 (2008)Astola, J., Haavisto, P., Neuvo, Y.: Vector median filters. Proc. IEEE 78, 678–689 (1990)Morillas, S., Gregori, V., Sapena, A.: Adaptive marginal median filter for colour images. Sensors 11, 3205–3213 (2011)Morillas, S., Gregori, V.: Robustifying vector median filter. Sensors 11, 8115–8126 (2011)Dillon, W.R., Goldstein, M.: Multivariate Analysis: Methods and Applications. Wiley, Hoboken (1984)Jackson, J.E.: A User’s Guide to Principal Components. Wiley, Hoboken (2003)Camacho, J., Picó, J.: Multi-phase principal component analysis for batch processes modelling. Chemom. Intell. Lab. Syst. 81, 127–136 (2006)Nomikos, P., MacGregor, J.: Multivariate SPC charts for monitoring batch processes. Technometrics 37, 41–59 (1995)Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)Grecova, Svetlana, Morillas, Samuel: Perceptual similarity between color images using fuzzy metrics. J. Vis. Commun. Image Represent. 34, 230–235 (2016)Fairchild, M.D., Johnson, G.M.: iCAM framework for image appearance differences and quality. J. Electron. Imaging 13(1), 126–138 (2004)Immerkaer, J.: Fast noise variance estimation. Comput. Vis. Image Underst. 64, 300–302 (1996

Ranking News-Quality Multimedia

News editors need to find the photos that best illustrate a news piece and fulfill news-media quality standards, while being pressed to also find the most recent photos of live events. Recently, it became common to use social-media content in the context of news media for its unique value in terms of immediacy and quality. Consequently, the amount of images to be considered and filtered through is now too much to be handled by a person. To aid the news editor in this process, we propose a framework designed to deliver high-quality, news-press type photos to the user. The framework, composed of two parts, is based on a ranking algorithm tuned to rank professional media highly and a visual SPAM detection module designed to filter-out low-quality media. The core ranking algorithm is leveraged by aesthetic, social and deep-learning semantic features. Evaluation showed that the proposed framework is effective at finding high-quality photos (true-positive rate) achieving a retrieval MAP of 64.5% and a classification precision of 70%.Comment: To appear in ICMR'1

Retinex filtering of foggy images: generation of a bulk set with selection and ranking

In this paper we are proposing the use of GIMP Retinex, a filter of the GNU Image Manipulation Program, for enhancing foggy images. This filter involves adjusting four different parameters to find the output image which has to be preferred according to some specific purposes. Aiming to obtain a processing, which is able of choosing automatically the best image from a given set, we are proposing a method for the generation a bulk set of GIMP Retinex filtered images and a preliminary approach for selecting and ranking them.Comment: Keywords: GIMP Retinex, GIMP, Image processing, Bulk generation of images, Bulk manipulation of image

Video Propagation Networks

We propose a technique that propagates information forward through video data. The method is conceptually simple and can be applied to tasks that require the propagation of structured information, such as semantic labels, based on video content. We propose a 'Video Propagation Network' that processes video frames in an adaptive manner. The model is applied online: it propagates information forward without the need to access future frames. In particular we combine two components, a temporal bilateral network for dense and video adaptive filtering, followed by a spatial network to refine features and increased flexibility. We present experiments on video object segmentation and semantic video segmentation and show increased performance comparing to the best previous task-specific methods, while having favorable runtime. Additionally we demonstrate our approach on an example regression task of color propagation in a grayscale video.Comment: Appearing in Computer Vision and Pattern Recognition, 2017 (CVPR'17