Non-rigid registration on histopathological breast cancer images using deep learning

Cancer is one of the leading causes of death in the world, in particular, breast cancer is the most frequent in women. Early detection of this disease can significantly increase the survival rate. However, the diagnosis is difficult and time-consuming. Hence, many artificial intelligence applications have been deployed to speed up this procedure. In this MSc thesis, we propose an automatic framework that could help pathologists to improve and speed up the first step of the diagnosis of cancer. It will facilitate the cross-slide analysis of different tissue samples extracted from a selected area where cancer could be present. It will allow either pathologists to easily compare tissue structures to understand the disease's seriousness or the automatic analysis algorithms to work with several stains at once. The proposed method tries to align pairs of high-resolution histological images, curving and stretching part of the tissue by applying a deformation field to one image of the pair

LAP-based motion-compensated frame interpolation

High-quality video frame interpolation often necessitates accurate motion estimates between consecutive frames. Standard video encoding schemes often estimate the motion between frames using variants of block matching algorithms. For the sole purposes of video frame interpolation, more accurate estimates can be obtained using modern optical flow methods. In this thesis, we use the recently proposed Local All-Pass (LAP) algorithm to compute the optical flow between two consecutive frames. The resulting flow field is used to perform interpolation using cubic splines. We compare the interpolation results against a well-known optical flow estimation algorithm as well as against a recent convolutional neural network scheme for video frame interpolation. Qualitative and quantitative results show that the LAP algorithm performs fast, high-quality video frame interpolation, and perceptually outperforms the neural network and the Lucas-Kanade method on a variety of test sequences. We also perform a case study to compare LAP interpolated frames against those obtained using two leading methods on the Middlebury optical flow benchmark. Finally, we perform a user study to gauge the correlation between the quantitative and qualitative results.Ope

Local All-Pass Geometric Deformations

This paper deals with the estimation of a deformation that describes the geometric transformation between two images. To solve this problem, we propose a novel framework that relies upon the brightness consistency hypothesis-a pixel's intensity is maintained throughout the transformation. Instead of assuming small distortion and linearizing the problem (e.g. via Taylor Series expansion), we propose to interpret the brightness hypothesis as an all-pass filtering relation between the two images. The key advantages of this new interpretation are that no restrictions are placed on the amplitude of the deformation or on the spatial variations of the images. Moreover, by converting the all-pass filtering to a linear forward-backward filtering relation, our solution to the estimation problem equates to solving a linear system of equations, which leads to a highly efficient implementation. Using this framework, we develop a fast algorithm that relates one image to another, on a local level, using an all-pass filter and then extracts the deformation from the filter-hence the name "Local All-Pass" (LAP) algorithm. The effectiveness of this algorithm is demonstrated on a variety of synthetic and real deformations that are found in applications, such as image registration and motion estimation. In particular, when compared with a selection of image registration algorithms, the LAP obtains very accurate results for significantly reduced computation time and is very robust to noise corruption.</p

Local all-pass geometric deformations

This paper deals with the estimation of a deformation that describes the geometric transformation between two images. To solve this problem, we propose a novel framework that relies upon the brightness consistency hypothesis - a pixel &amp;amp; #x2019;s intensity is maintained throughout the transformation. Instead of assuming small distortion and linearising the problem (e.g. via Taylor Series expansion), we propose to interpret the brightness hypothesis as an all-pass filtering relation between the two images. The key advantages of this new interpretation are that no restrictions are placed on the amplitude of the deformation or on the spatial variations of the images. Moreover, by converting the all-pass filtering to a linear forward-backward filtering relation, our solution to the estimation problem equates to solving a linear system of equations, which leads to a highly efficient implementation. Using this framework, we develop a fast algorithm that relates one image to another, on a local level, using an all-pass filter and then extracts the deformation from the filter &amp;amp; #x2014;hence the name &amp;amp; #x201C;Local All-Pass &amp;amp; #x201D; (LAP) algorithm. The effectiveness of this algorithm is demonstrated on a variety of synthetic and real deformations that are found in applications such as image registration and motion estimation. In particular, the LAP obtains very accurate results for significantly reduced computation time when compared to a selection of image registration algorithms and is very robust to noise corruption