Artificial intelligent classification of biomedical color image using quaternion discrete radial Tchebichef moments
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Artificial intelligent classification of biomedical color image using quaternion discrete radial Tchebichef moments Hicham Amakdouf 1 & Amal Zouhri 1 & Mostafa El Mallahi 2 Driss Chenouni 2 & Hassan Qjidaa 1
2
& Ahmed Tahiri &
Received: 13 February 2020 / Revised: 18 August 2020 / Accepted: 28 August 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract
In this work, we propose a new set of an intelligent classifier of biomedical color images using Quaternion Discrete Radial Tchebichef Moments (QRTM). This moment has shown very high robustness in terms of color image representation. Thus, we propose a new approach for the fast and accurate reconstruction of multi-level and color images. This approach based on the reconstruction of color images by applying matrix computation. In the second step, we propose a new method for the extraction of Quaternion Discrete Radial Tchebichef Moments invariant (QRTMI). This method based on the representation of the extraction of these invariants. The performance of the invariance of these moments under the three types of geometrical transformations (Rotation, translation, scale) are very important. Finally, we present a new model based on a Multilayer perceptron (MLP) for the classification of biomedical color images. The experimental results show that the QRTMI very powerful compared with radial Legendre - Fourier and Quaternion discrete radial Krawtchouk moments invariant for the biomedical colors images using the BreaKHis Database (Breast Tumor). Keywords Color image representation . Color image reconstruction . Image classification . Quaternion
1 Introduction A few year ago, with the fast progress of mathematics, artificial intelligent and learning machine of digital cameras, almost all of the medical images are chromatic. Indeed, to transmit or stock more information, the digital color images have the potential than a gray level or binary image. Moreover, the values associated of three colors such as green, blue, and red for * Mostafa El Mallahi [email protected] Extended author information available on the last page of the article
Multimedia Tools and Applications
each level of the pixel or as well its hue, brightness, and saturation, can be successful used in many images processing tasks such as reconstruction, object classification [1], recognition, registration, and segmentation. The traditional approach to treatment with digital color images has in processing each level separately, employing a gray level method, and to combine the individual output results. As consequence, this method misses the inherent correlation between the entities of three colors level [17]. The main problem is therefore to handle three values of each pixel level in totality. On the other hand, some authors present the video description as a succession of color images [7, 23, 24], which is one of the ultimate goals for video understanding. For resolving this problem, recently, algebra quaternion proposed in digital color image analysis to represent
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