Index

Detecting Defects on Transparent Objects using Polarization Cameras

The classification of images is a well known task in computer vision. However, transparent or semi-
transparent objects have several properties that can make computer vision tasks harder. Those objects
usually have less textures and sometimes strong reflections. Occasionally, different backgrounds make
it hard to recognize edges or the shape of an object. [1, 2]
To overcome these difficulties we use polarization cameras in this work. In contrast to ordinary cameras,
polarization cameras additionally record information about the polarization of the light rays. Most
natural light sources emit unpolarized light. By using a light source that emits polarized light, it is
possible to remove reflections or increase the contrast. Further it is known that the Angle of Linear
Polarization (AoLP) provides information about the normal of a surface [3].
In this work, we will use the Deep Learning approach and use Convolutional Neural Networks (CNNs)
to explore the following topics:
1. Comparison of different sorts of preprocessing:
• Using only raw data / reshaped raw data
• Using extra features Degree of Linear Polarization (DoLP) and AoLP
2. Influence of different light sources.
3. Comparison of different defect classes.
To evaluate the results we use different error metrics such as accuracy and f1, as well as gradient class
activation maps (GradCAM) [4].
The implementation should be done in Python.

References
[1] Agastya Kalra, Vage Taamazyan, Supreeth Krishna Rao, Kartik Venkataraman, Ramesh Raskar, and Achuta
Kadambi. Deep Polarization Cues for Transparent Object Segmentation. In 2020 IEEE/CVF Conference on
Computer Vision and Pattern Recognition (CVPR), pages 8599–8608, Seattle, WA, USA, June 2020. IEEE.
[2] Ilya Lysenkov, Victor Eruhimov, and Gary Bradski. Recognition and Pose Estimation of Rigid Transparent
Objects with a Kinect Sensor. page 8, 2013.
[3] Francelino Freitas Carvalho, Carlos Augusto de Moraes Cruz, Greicy Costa Marques, and Kayque Martins
Cruz Damasceno. Angular Light, Polarization and Stokes Parameters Information in a Hybrid Image Sensor
with Division of Focal Plane. Sensors, 20(12):3391, June 2020.[4] Ramprasaath R. Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and
Dhruv Batra. Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization.
International Journal of Computer Vision, 128(2):336–359, February 2020.

Diffeomorphic MRI Image Registration using Deep Learning

State-of-the-art deformable image registration approaches achieve impressive results and are commonly used in diverse image processing applications. However, these approaches are computationally expensive even on GPUs [1] due to their requirement to solve an optimization problem for each image pair during registration [2]. Most Learning based methods either required labeled data or do not guarantee a diffeomorphic registration or deformation field reversibility [1]. Adrian V. Dalca et. al. presented an unsupervised Deep-Learning framework for diffeomorphic image registration named Voxelmorph in [1].
In this thesis the network described in [1] will be implemented and trained on Cardiac Magnetic Resonance images to build an application for fast diffeomorphic image registration. The results will be compared to state-of-the-art diffeomorphic image registration methods. Additionally the method will be evaluated by comparing segmented areas as well as landmark locations of co-registered images. Furthermore the method in [1] will be extended to a one-to-many registration method using the approach in [3] to fulfill the desire for motion estimation of anatomy of interest for increasingly available dynamic imaging data [3]. Data used in this thesis will be provided by Siemens Healthineers. The implementation will be done using a open source framework like PyTorch [4].

The thesis will include the following points:

• Literature research of the topic of state-of-the-art methods regarding diffeomorphic image registration and one to many registration

• Implementing a Neural Network for diffeomorphic image regstration and extending it to a one-to-many registration

• Comparison of the results with state-of-the-art image registration methods

[1] Balakrishnan, G., Zhao, A., Sabuncu, M. R., Guttag, J. V. & Dalca, A. V. VoxelMorph: A Learning Framework for
Deformable Medical Image Registration. CoRR abs/1809.05231. arXiv: 1809.05231. http://arxiv.org/abs/1809.05231 (2018).
[2] Ashburner, J. A fast diffeomorphic image registration algorithm. NeuroImage 38, 95 –113. ISSN: 1053-8119. http://www.sciencedirect.com/science/article/pii/S1053811907005848 (2007).
[3] Metz, C., Klein, S., Schaap, M., van Walsum, T. & Niessen, W. Nonrigid registration of dynamic medical imaging data using nD+t B-splines and a groupwise optimization approach. Medical Image Analysis 15, 238 –249. ISSN: 1361-8415. http://www.sciencedirect.com/science/article/pii/S1361841510001155 (2011).
[4] Paszke, A. et al. PyTorch: An Imperative Style, High-Performance Deep Learning Library. CoRR abs/1912.01703. arXiv: 1912.01703. http://arxiv.org/abs/1912.01703 (2019).

Content-based Image Retrieval based on compositional elements for art historical images

Absorption Image Correction in X-ray Talbot-Lau Interferometry for Reconstruction

X-ray Phase-Contrast Imaging (PCI) is an imaging technique that measures the refraction of X-rays created by an object. There are several ways to realize PCI, such as interferometric and analyzer-based methods [3]. In contrast to X-ray absorption imaging, the phase image provides high soft-tissue contrast.
The implementation by a grating-based interferometer enables measuring an X-ray absorption image, a differential phase image and a dark-field image [2, p. 192-205]. Felsner et al. proposed the integration of a Tablot-Lau Interferometer (TLI) into an existing clinical CT system [1]. Three different gratings are mounted between the X-ray tube and the detector: two in front of the object, one behind (see Fig. 1). Currently it is not possible to install gratings with a diameter of more than a few centimeters because of various reasons [1]. The consequence is that it is only possible to create a phase-contrast image for a small area.
Nevertheless, for capturing the absorption image the entire size of the detector can be used. However, the absorption image is influenced by the gratings as they induce inhomogeneous exposure of the X-ray detector.
Besides that, the intensity values change with each projection. The X-ray tube, detector and gratings are rotating around the object during the scanning process. Depending on their position, parts of the object are covered by grating G 1 for one period of the rotation but not always.
It is expected that the part of the absorption image covered by the gratings differs from the rest of the image in its intensity values. Also, a sudden change in the intensity values can be detected at the edge of the lattice. This may lead to artifacts in 3-D reconstruction.
In this work, we will investigate the anticipated artifacts in the reconstruction and implement (at least) one correction algorithm. Furthermore, the reconstruction results with and without a correction algorithm will be evaluated using simulated and/or real data.

References:
[1] L. Felsner, M. Berger, S. Kaeppler, J. Bopp, V. Ludwig, T. Weber, G. Pelzer, T. Michel, A. Maier, G. Anton, and C. Riess. Phase-sensitive region-of-interest computed tomography. In International Conference on Medical Image Computing and Computer-Assisted Intervention, pages 137–144, Cham, 2018. Springer.
[2] A. Maier, S. Steidl, V. Christlein, and J. Hornegger. Medical Imaging Systems: An Introductory Guide, volume 11111. Springer, Cham, 2018.
[3] F. Pfeiffer, T. Weitkamp, O. Bunk, and C. David. Phase retrieval and differential phase-contrast imaging with low-brilliance X-ray sources. Nature Physics, 2(4):258–261, 2006.

Truncation-correction Method for X-ray Dark-field Computed Tomography

Grating-based imaging provides three types of images, an absorption, differential phase and dark-field image. The dark-field image provides structural information about the specimen at the micrometer and sub-micrometer scale. A dark-field image can be measured by a X-ray grating interferometer. For example the Talbot-Lau interferometer that consists of three gratings. Due to the small size of the gratings, truncation arises in the projection images. This becomes an issue, since it leads to artifacts in the reconstruction.

This Bachelor thesis aims to reduce truncation artifacts of dark-field reconstructions. Inspired by the method proposed by Felsner et al. [1] the truncated dark-field image will be corrected by using the information of a complete absorption image. To describe the correlation between absorption and the dark-field signal, the decomposition by Kaeppler et al. [2] will be used. The dark-field correction algorithm will be implemented in an iterative scheme and a parameter search and evaluation of the method will be conducted.

References:
[1] Lina Felsner, Martin Berger, Sebastian Kaeppler, Johannes Bopp, Veronika Ludwig, Thomas Weber, Georg Pelzer, Thilo Michel, Andreas Maier, Gisela Anton, and Christian Riess. Phase-sensitive region-of-interest computed tomography. In Medical Image Computing and Computer Assisted Intervention – MICCAI 2018, pages 137–144, Cham, 2018. Springer International Publishing.
[2] Sebastian Kaeppler, Florian Bayer, Thomas Weber, Andreas Maier, Gisela Anton, Joachim Hornegger, Matthias Beckmann, Peter A. Fasching, Arndt Hartmann, Felix Heindl, Thilo Michel, Gueluemser Oezguel, Georg Pelzer, Claudia Rauh, Jens Rieger, Ruediger Schulz-Wendtland, Michael Uder, David Wachter, Evelyn Wenkel, and Christian Riess. Signal decomposition for x-ray dark-field imaging. In Medical Image Computing and Computer Assisted Intervention – MICCAI 2014, pages 170–177, Cham, 2014. Springer International Publishing.

Automated analysis of Parkinson’s Disease on the basis of evaluation of handwriting

In this thesis current state-of-the-art methods of automatic analysis of Parkinson’s disease (PD) are tested along with new ideas of signal processing. Since there is currently no cure for PD, it is important to introduce methods for automatic monitoring and analysis. Therefore handwriting-samples of 49 healthy subjects and 75 PD patients acquired with a graphic tablet are used. Those subjects performed different drawing tasks. With a kinematic analysis
accuracies of up 77% are achieved when using one task alone and accuracies up to 86% are achieved when combining different tasks. A newly developed spectral analysis resulted in scores of up to 96% for an individual task. Combining the spectral features of a standalone task with features from different tasks or a different analysis did not lead to better results. Making predictions about the severity of the disease based on the features acquired for the bi-class problem failed. An attempt was made modeling the velocity profile of strokes with lognormal distributions and using the thereby obtained parameters for classification. Because of difficulties with the modeling of strokes with different lengths, a classification failed.

Characterizing ultraound images through breast density related features using traditional and deep learning approaches

Breast cancer is the most common cancer in women worldwide with accounting for almost a quarter of all new female cancer cases [ 1 ]. In order to improve the chances of recovery and reduce mortality, it is crucial to detect and diagnose it as early as possible. Mammography is the standard treatment when screening for breast cancer. While mammography images have an important role in cancer diagnosis, it has been shown that the sensitivity of these images decreases with a high mammographic density [2].The mammographic density (MD) refers to the amount of fibroglandular tissue in the breast in proportion to the amount of fatty tissue. MD is an established risk factor for breast cancer. The general risk for developing breast cancer is increased with a higher density. Women with a density of 25% or higher are twice as likely to develop breast cancer, with 75% even five times, compared to women with a MD of less than 5% [3]. In addition there is a possibility that in dense breast a tumor may be masked on a mammogram [2]. Therefore it is necessary to consider the breast’s density when screening for breast cancer. Several studies aimed at supporting and improving breast cancer diagnosis with computer aided systems and feature evaluation and such studies have taken the MD into consideration when evaluating mammography images [4][5].
In order to detect those tumors that are masked on mammography or support inconclusive findings, often an additional ultrasound (US) is conducted on women with high MD [6]. However US images underlie a high inter-observer variability. Computer-aided diagnosis aims to develop a method to analyze US images and support diagnosis with the aim of reducing this variability. The approach of this thesfa is to transfer and ad just the methods designed by Häberle et al. [4] used for characterizing 2-D mammographic images in order to use them on 3-D ultrasound images while only focusing on features correlating with the MD.
Additionally, more features will be generated using deep leaming, as most of the recent computer-aided diagnosis tools do not rely on traditional methods anymore. Over the last years deep learning has become the standard when working in medical imaging and several studies have shown a promising perf ormance when working with breast ultrasound images [7][8].
U sing both traditional and deep leaming methods for extracting features aims to improve the classification of possibly cancerous tissue by building a reliable set of features which characterize the MD of the patient. Furthermore, the traditional features may help to interpret those generated through deep learning approaches, in turn, the latter may help to show the benefit of using deep leaming when analyzing medical images.
This thesis will cover the following points:

• Literature review of mammographic density as a risk factor for breast cancer and ultrasound as an additional screening method
• Extraction and evaluation of a variety of automated features in ultrasound images using traditional and deep leaming approaches
• Analyzing the relationship of the extracted features with the mammographic density

References

] F. Bray, J. Ferlay, I. Soerjomataram, R. L. Siegel, L. A. Torre, and A. Jemal, “Global cancer statistics 2018: Globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries,” CA: a cancer joumal for clinicians, vol. 68, no.6,pp.394-424,2018.[2] P. E. Freer, “Mammographie breast density: impact on breast cancer risk and implications for screening,” Radiographics : a review publication of the Radiological Society of NorthAmerica, Inc, vol. 35, no. 2, pp. 302-315, 2015.
[3] V. A. McCormack, “Breast density and parenchymal pattems as markers of breast cancer risk: A meta-analysis,” Cancer Epidemiology and Prevention Biomarkers, vol. 15, no. 6, pp. 1159-1169, 2006.
[4] L. Häberle, F. Wagner, P. A. Fasching, S. M. Jud, K. Heusinger, C. R. Loehberg, A. Hein, C. M. Bayer, C. C. Hack, M. P. Lux, K. Binder, M. Elter, C. Milnzenmayer, R. Schulz-Wendtland, M. Meier-Meitinger, B. R. Adamietz, M. Uder, M. W. Beckmann, and T. Wittenberg, “Characterizing mammographic images by using generic texture features,” Breast cancer research: BCR, vol. 14, no. 2, 2012.
[5] M. Tan, F. Aghaei, Y. Wang, and B. Zheng, “Developing a new case based computer-aided detection scheme and an adaptive cueing method to improve performance in detecting mammographic lesions,” Physics in medicine and biology, vol. 62, no. 2, pp.358-376,2017.
[6] L. Häberle, C. C. Hack, K. Heusinger, F. Wagner, S. M. Jud, M. Uder, M. W. Beckmann, R. Schulz-Wendtland, T. Wittenberg, and P. A. Fasching, “Using automated texture features to determine the probability for masking ofa tumor on mammography, but not ultrasound,” Europeanjoumal of medical research, vol. 22, no. 1, 2017.
[7] H. Tanaka, S.-W. Chiu, T. Watanabe, S. Kaoku, and T. Yamaguchi, “Computer-aided diagnosis system for breast ultrasound images using deep Ieaming,” Physics in medicine and biology, vol. 64, no. 23, 2019.
[8] M. H. Yap, G. Pons, J. Marti, S. Ganau, M. Sentis, R. Zwiggelaar, A. K. Davison, R. Marti, and H. Y. Moi, “Automated breast ultrasound lesions detection using convolutional neural networks,” IEEEjoumal of biomedical and health informatics, vol.22,no.4,pp. 1218-1226,2018.

CITA: An Android-based Application to Evaluate the Speech of Cochlear Implant Users

Cochlear Implants (CI) are the most suitable devices for severe and profound deafness when hearing aids do not improve sufficiently speech perception. However, CI users often present altered speech production and limited understanding even after hearing rehabilitation. People suffering from severe to profound deafness may experience different speech disorders such as decreased intelligibility, changes in terms of articulation, slower speaking rate. among others. Though hearing outcome is regularly measured after cochlear implantation, speech production quality is seldom assessed in outcome evaluations. This project aims to develop an Android application suitable to analyze the speech production and perception of CI users.

Multimodal Breast Cancer Detection using a Fusion of Ultrasound and Mammogram Features

Age Estimation on Panoramic Dental X-ray Images Using Deep Learning