Face Recognition Methods Based on Feedforward Neural Networks, Principal Component Analysis and Self-Organizing Map

In this contribution, human face as biometric is considered. Original method of feature extraction from image data is introduced using MLP (multilayer perceptron) and PCA (principal component analysis). This method is used in human face recognition system and results are compared to face recognition system using PCA directly, to a system with direct classification of input images by MLP and RBF (radial basis function) networks, and to a system using MLP as a feature extractor and MLP and RBF networks in the role of classifier. Also a two-stage method for face recognition is presented, in which Kohonen self-organizing map is used as a feature extractor. MLP and RBF network are used as classifiers. In order to obtain deeper insight into presented methods, also visualizations of internal representation of input data obtained by neural networks are presented

Application of Shape-Independent Orthogonal Transforms for Image Interpolation

In the contribution we develop a new method for object-oriented interpolation of images. It is an important tool in image processing, since using interpolation we can considerably decrease amount of data, necessary for image reconstruction. Application of this interpolation enables to down-sample the object separately. The selected object can be processed at the different sampling level. This approach allows object oriented zoom, for example. Moreover, object - oriented approach is a very novel idea that helps to understand the content of image. Method is created from cosine approximation implemented to coding with shape - independent basis functions

New Statistics for Texture Classification Based on Gabor Filters

The paper introduces a new method of texture segmentation efficiency evaluation. One of the well known texture segmentation methods is based on Gabor filters because of their orientation and spatial frequency character. Several statistics are used to extract more information from results obtained by Gabor filtering. Big amount of input parameters causes a wide set of results which need to be evaluated. The evaluation method is based on the normal distributions Gaussian curves intersection assessment and provides a new point of view to the segmentation method selection

Error Concealment using Neural Networks for Block-Based Image Coding

In this paper, a novel adaptive error concealment (EC) algorithm, which lowers the requirements for channel coding, is proposed. It conceals errors in block-based image coding systems by using neural network. In this proposed algorithm, only the intra-frame information is used for reconstruction of the image with separated damaged blocks. The information of pixels surrounding a damaged block is used to recover the errors using the neural network models. Computer simulation results show that the visual quality and the MSE evaluation of a reconstructed image are significantly improved using the proposed EC algorithm. We propose also a simple non-neural approach for comparison

Characterization of murine prostate organoids as a preclinical model for prostate cancer

Krebs stellt heutzutage ein großes Gesundheitsproblem dar und ist jedes Jahr weltweit für viele Todesfälle verantwortlich. Prostatakrebs ist in mehr als der Hälfte der Länder weltweit der am häufigsten diagnostizierte Krebs bei Männern. Die Forschungsmittel, die in die Untersuchung von Krebs und seine Eigenschaften investiert worden sind, um die Ursachen und Pathologien aufzudecken und neue Therapien zu entwickeln, haben in den letzten Jahrzehnten zugenommen. Obwohl die Prävalenz von Prostatakrebs relativ hoch ist, hat das Fehlen geeigneter In-vitro-Modellsysteme den Fortschritt der Prostatakrebsforschung eingeschränkt. Während in den letzten Jahrzehnten 2D-Zellkultursysteme und Tiermodellsysteme die Grundlage für Prostatakrebsstudien waren, haben sich im Laufe der Zeit neue Möglichkeiten und Technologien herausgebildet. Beispielsweise hat die neuartige Organoidtechnologie in den letzten Jahren aufgrund ihrer großen Anwendungen in der Krebsforschung Aufmerksamkeit erregt. Bei Prostata-Organoiden, die aus dem primärem Prostatakrebs des Menschen stammen, hat es sich als schwierig erwiesen, über einen langen Zeitraum in der Kultur zu bleiben. Organoide des primären Prostatakrebses der Maus wurden jedoch erfolgreich etabliert und sind ein alternatives In-vitro Modellsystem zur Untersuchung des menschlichen Prostatakrebses. Um dieses neuartige Modellsystem zu nutzen, haben wir eine Gruppe von gutartigen und bösartigen Prostataepithelorganoiden der Maus eingerichtet, die verschiedene genetische Knockouts von Prostatakrebs-relevanten Genen enthalten, wie einen einzelnen Knockout von Pten und einen doppelten Knockout von Pten/Stat3 und Pten/Tp53. Wir haben diese organoiden Kulturen mit histologischer HE-Färbung und verschiedenen immunhistochemischen Färbungen wie Ki67, pAKT, STAT3 und CK8 charakterisiert. Wir zeigen, dass sowohl gutartige als auch bösartige Organoide ihrem Ursprungsgewebe histologisch und auch hinsichtlich ihrer Signalwege stark ähneln. Wir haben auch In-vitro Knockouts von Pten- und Pten/Stat3-Genen aus Wildtyp-Mäusen mit floxierten Allelen unter Verwendung des Cre-lox-Rekombinationssystems in vitro erzeugt. Wir zeigen, dass die Deletion von Pten und Pten/Stat3 den Wildtyp im Laufe der Zeit in maligne Organoide umwandelt und die in vivo beobachteten Prostatakrebs-Phänotypen nachahmt. Wir schlagen dieses In-vitro-Knockout-System als neues Modellsystem zur Untersuchung der Tumorentstehung vor. Zusammenfassend lässt sich sagen, dass humane primäre Prostatakrebs-Organoide bisher nicht erfolgreich als Langzeitkulturen etabliert worden sind. Wir zeigen jedoch, dass murine Prostata-Organoide als geeignetes alternatives Modellsystem für Prostatakrebs dienen. In der Zukunft wollen wir Co-Kulturen etablieren, die auch die Immun- und Stromakomponenten des Prostatatumors enthalten, um die Genauigkeit dieses Modells als präklinisches Prostatakrebsmodell weiter zu erhöhen.Cancer represents a major today’s health problem and is responsible for many deaths globally each year with prostate cancer being the most diagnosed cancer in men in over half of the countries worldwide. The research invested in studying cancer and its properties has been on the rise in the last decades in order to uncover its causes, pathologies and to develop new treatments. Even though the prevalence of prostate cancer is relatively high, the lack of suitable in vitro model systems has hindered the progress of prostate cancer research. While in the last decades, 2D cell culture systems and animal model systems have been the basis of prostate cancer studies, new possibilities and technologies have emerged over time. For instance, the novel organoid technology gained attention in recent years for its large applications in cancer research. While prostate organoids derived from human primary prostate cancer have proven to be challenging to maintain in culture over long period of time, murine primary prostate cancer organoids have been successfully established and account for an alternative in vitro model system to study human prostate cancer. In order to exploit this novel model system, we established a panel of murine benign and malignant prostate epithelial organoids, harbouring different genetic knockouts of prostate cancer relevant genes such as a single knockout of Pten and double knockouts of Pten/Stat3 and Pten/Tp53. We characterized those organoid cultures with histological HE staining and various immunohistochemistry stainings such as Ki67, pAKT, STAT3 and CK8. We show that both benign and malignant organoids highly resemble their tissue of origin histologically and also regarding their signalling pathways. We also generated in vitro knockouts of Pten and Pten/Stat3 genes from wildtype mice with floxed alleles using the Cre-lox recombination system in vitro. We demonstrate that deletion of both Pten and Pten/Stat3 transform wildtype to malignant organoids over time and mimic the prostate cancer phenotypes that are seen in vivo. We propose this in vitro knockout system as a new model system to study tumorigenesis. In conclusion, while human primary prostate cancer organoids have not been successfully established as long term cultures so far, we show that murine prostate organoids serve as a suitable alternative model system for prostate cancer. In the future we aim to establish co-cultures containing also the immune and stromal components of the prostate tumour to further increase the accuracy of this model as a preclinical prostate cancer model

Frequency, Time, Representation and Modeling Aspects for Major Speech and Audio Processing Applications

There are many speech and audio processing applications and their number is growing. They may cover a wide range of tasks, each having different requirements on the processed speech or audio signals and, therefore, indirectly, on the audio sensors as well. This article reports on tests and evaluation of the effect of basic physical properties of speech and audio signals on the recognition accuracy of major speech/audio processing applications, i.e., speech recognition, speaker recognition, speech emotion recognition, and audio event recognition. A particular focus is on frequency ranges, time intervals, a precision of representation (quantization), and complexities of models suitable for each class of applications. Using domain-specific datasets, eligible feature extraction methods and complex neural network models, it was possible to test and evaluate the effect of basic speech and audio signal properties on the achieved accuracies for each group of applications. The tests confirmed that the basic parameters do affect the overall performance and, moreover, this effect is domain-dependent. Therefore, accurate knowledge of the extent of these effects can be valuable for system designers when selecting appropriate hardware, sensors, architecture, and software for a particular application, especially in the case of limited resources

Extraction of Facial Features from Color Images

In this paper, a method for localization and extraction of faces and characteristic facial features such as eyes, mouth and face boundaries from color image data is proposed. This approach exploits color properties of human skin to localize image regions – face candidates. The facial features extraction is performed only on preselected face-candidate regions. Likewise, for eyes and mouth localization color information and local contrast around eyes are used. The ellipse of face boundary is determined using gradient image and Hough transform. Algorithm was tested on image database Feret