Improvements on Gabor Descriptor Retrieval for Patch Detection

The localization of object parts in the component-based object detection is among the main tasks to solve. This paper presents several improvements of the proposed local image descriptor based on Gabor wavelets. Including these descriptors in the desired application is an ambitious challenge if we take into account the high number of parameters. Determining of parameters can be very hard because of their infinite definition range. Defining the filters is done in two stages: a theoretical consideration narrows the domain and the cardinality of parameters; this is followed by adequate experiments to select the most characteristic descriptor for a target image patch. The descriptor is created from a given number of 2D Gabor filters chosen by the GentleBoost learning algorithm. Comparing the proposed descriptor to those found in the state of the art, we can conclude that the selected filters are adaptable to any target object. In contrast to this, the majority of filter-based descriptors have fixed values for the parameters that do not allow to be ductile to the given object. Parameters fine-tuning allows the descriptor to be general, and discriminative at the same time. The effect of the following experiments has been analyzed during the investigation: elimination of redundancy between the weak classifiers, using the LoG interest points in the detection process. Finally, we propose an acceleration algorithm in order to deter- mine the response map faster. By means of the descriptor, the response map is created, which accurately localizes the target object part and can easily be integrated in almost all detection systems

Two-phase MRI brain tumor segmentation using random forests and level set methods

Magnetic resonance images (MRI) in various modalities contain valuable information usable in medical diagnosis. Accurate delimitation of the brain tumor and its internal tissue structures is very important for the evaluation of disease progression, for studying the effects of a chosen treatment strategy and for surgical planning as well. At the same time early detection of brain tumors and the determination of their nature have long been desirable in preventive medicine. The goal of this study is to develop an intelligent software tool for quick detection and accurate segmentation of brain tumors from MR images. In this paper we describe the developed two-staged image segmentation framework. The first stage is a voxelwise classifier based on random forest (RF) algorithm. The second acquires the accurate boundaries by evolving active contours based on the level set method (LSM). The intelligent combination of two powerful segmentation algorithms ensures performances that cannot be achieved by either of these methods alone. In our work we used the MRI database created for the BraTS ’14-‘16 challenges, considered a gold standard in brain tumor segmentation. The segmentation results are compared with the winning state of the art methods presented at the Brain Tumor Segmentation Grand Challenge and Workshop (BratsTS)

U-Net architecture variants for brain tumor segmentation of histogram corrected images

In this paper we propose to create an end-to-end brain tumor segmentation system that applies three variants of the well-known U-Net convolutional neural networks. In our results we obtain and analyse the detection performances of U-Net, VGG16-UNet and ResNet-UNet on the BraTS2020 training dataset. Further, we inspect the behavior of the ensemble model obtained as the weighted response of the three CNN models. We introduce essential preprocessing and post-processing steps so as to improve the detection performances. The original images were corrected and the different intensity ranges were transformed into the 8-bit grayscale domain to uniformize the tissue intensities, while preserving the original histogram shapes. For post-processing we apply region connectedness onto the whole tumor and conversion of background pixels into necrosis inside the whole tumor. As a result, we present the Dice scores of our system obtained for WT (whole tumor), TC (tumor core) and ET (enhanced tumor) on the BraTS2020 training dataset

Performance analysis of eigenface recognition under varying external conditions

In the field of image processing and computer vision face recognition is one of the most studied research domain. It has large variety of applications in different areas like security and surveillance systems, identification and authentication etc. In this paper we propose to analyze the face recognition system based on the eigenface[22] method under different conditions. The eigenface method is a statistical dimensionality reduction method, which obtains the adequate face space, out of a given training database. The idea of observing the performances i.e. the recognition rate in different situations (like presence or absence of important facial features such as glasses or beard) came from the diploma work [20]. The experiments described in this article study the recognition performance of the algorithm, by varying the number of considered feature vectors. Beside of these, we studied the behavior of such a system if the analyzed individual is wearing glasses or beard. Finally, we concentrate on carrying out experiments for noisy images by adding common types of noise like salt & pepper noise, Gaussian noise or Poisson noise to every test image

Boosting Unsupervised Dorsal Hand Vein Segmentation with U-Net Variants

The identification of vascular network structures is one of the key fields of research in medical imaging. The segmentation of dorsal hand vein patterns form NIR images is not only the basis for reliable biometric identification, but would also provide a significant tool in assisting medical intervention. Precise vein extraction would help medical workers to exactly determine the needle entry point to efficiently gain intravenous access for different clinical purposes, such as intravenous therapy, parenteral nutrition, blood analysis and so on. It would also eliminate repeated attempts at needle pricks and even facilitate an automatic injection procedure in the near future. In this paper, we present a combination of unsupervised and supervised dorsal hand vein segmentation from near-infrared images in the NCUT database. This method is convenient due to the lack of expert annotations of publicly available vein image databases. The novelty of our work is the automatic extraction of the veins in two phases. First, a geometrical approach identifies tubular structures corresponding to veins in the image. This step is considered gross segmentation and provides labels (Label I) for the second CNN-based segmentation phase. We visually observe that different CNNs obtain better segmentation on the test set. This is the reason for building an ensemble segmentor based on majority voting by nine different network architectures (U-Net, U-Net++ and U-Net3+, all trained with BCE, Dice and focal losses). The segmentation result of the ensemble is considered the second label (Label II). In our opinion, the new Label II is a better annotation of the NCUT database than the Label I obtained in the first step. The efficiency of computer vision algorithms based on artificial intelligence algorithms is determined by the quality and quantity of the labeled data used. Furthermore, we prove this statement by training ResNet–UNet in the same manner with the two different label sets. In our experiments, the Dice scores, sensitivity and specificity with ResNet–UNet trained on Label II are superior to the same classifier trained on Label I. The measured Dice scores of ResNet–UNet on the test set increase from 90.65% to 95.11%. It is worth mentioning that this article is one of very few in the domain of dorsal hand vein segmentation; moreover, it presents a general pipeline that may be applied for different medical image segmentation purposes

Comparison of Boosted Gabor Feature based Local Descriptor

AbstractIn the domain of computer vision boosting has become a very powerful tool. The method is used to form a strong classifier applied in several applications of pattern recognition and machine vision. Boosting is a sequential algorithm which separates the instances by the selection of weak classifiers and adds them to a final classifier, thereafter modifies the weights of different training data samples and applies the same classification algorithm in iterative way. The final decision is made by the so called final classifier, the responses of which are applied in a weighted voting decision.In this approach we start from a part-based object detection system described in previous articles [14.15]. The developed patch descriptor is based on two-dimensional Gabor wavelets. The Gabor filters describe the neighborhood of a given image pixel in two-dimensional space. From these local descriptors we created different weak classifiers which are used in the training phase of boosting. We have chosen the boosting algorithm for classification because, in the last decade, the best classification results have been obtained by this algorithm in the domain.In this paper we compare three classification methods based on the boosting approach. The first is Discrete AdaBoost, which considers discrete outputs (+1 for objects,-1 or 0 for non-objects). The second, GentleBoost, this algorithm minimizes the exponential loss and returns real values as classification responses. The third, LogitBoost is the fitting of an additive symmetric logistic regression model by log-likelihood to the training set and solves this optimization with Newton numerical method. Finally, we make a comparison of these classifiers in order to draw conclusions regarding the detection rate, false detection rate and other classification measures

Applications of different CNN architectures for palm vein identification

In this paper a palm vein identification system is presented, which exploits the strength of convolutional neural network (CNN) architectures. We built and compared six different CNN approaches for biometric identification based on palm images. Four of them were developed by applying transfer learning and fine-tuning techniques to relevant deep learning architectures in the literature (AlexNet, VGG-16, ResNet-50 and SqueezeNet). We proposed and analysed two novel CNN architectures as well. We experimentally compared the identification accuracy and training convergence of these models. Each model was trained and evaluated using the PUT palm vein near infrared image database. To increase the accuracy obtained, we investigated the influence of some image quality enhancement methods, such as contrast adjustment and normalization, Gaussian smoothing, contrast limited adaptive histogram equalization, and Hessian matrix based coarse vein segmentation. Results show high recognition accuracy for almost every such CNN-based approach

Boosting Unsupervised Dorsal Hand Vein Segmentation with U-Net Variants

The identification of vascular network structures is one of the key fields of research in medical imaging. The segmentation of dorsal hand vein patterns form NIR images is not only the basis for reliable biometric identification, but would also provide a significant tool in assisting medical intervention. Precise vein extraction would help medical workers to exactly determine the needle entry point to efficiently gain intravenous access for different clinical purposes, such as intravenous therapy, parenteral nutrition, blood analysis and so on. It would also eliminate repeated attempts at needle pricks and even facilitate an automatic injection procedure in the near future. In this paper, we present a combination of unsupervised and supervised dorsal hand vein segmentation from near-infrared images in the NCUT database. This method is convenient due to the lack of expert annotations of publicly available vein image databases. The novelty of our work is the automatic extraction of the veins in two phases. First, a geometrical approach identifies tubular structures corresponding to veins in the image. This step is considered gross segmentation and provides labels (Label I) for the second CNN-based segmentation phase. We visually observe that different CNNs obtain better segmentation on the test set. This is the reason for building an ensemble segmentor based on majority voting by nine different network architectures (U-Net, U-Net++ and U-Net3+, all trained with BCE, Dice and focal losses). The segmentation result of the ensemble is considered the second label (Label II). In our opinion, the new Label II is a better annotation of the NCUT database than the Label I obtained in the first step. The efficiency of computer vision algorithms based on artificial intelligence algorithms is determined by the quality and quantity of the labeled data used. Furthermore, we prove this statement by training ResNet–UNet in the same manner with the two different label sets. In our experiments, the Dice scores, sensitivity and specificity with ResNet–UNet trained on Label II are superior to the same classifier trained on Label I. The measured Dice scores of ResNet–UNet on the test set increase from 90.65% to 95.11%. It is worth mentioning that this article is one of very few in the domain of dorsal hand vein segmentation; moreover, it presents a general pipeline that may be applied for different medical image segmentation purposes

CNN Approaches for Dorsal Hand Vein Based Identification

In this paper we present a dorsal hand vein recognition method based on convolutional neural networks (CNN). We implemented and compared two CNNs trained from end-to-end to the most important state-of-the-art deep learning architectures (AlexNet, VGG, ResNet and SqueezeNet). We applied the transfer learning and finetuning techniques for the purpose of dorsal hand vein-based identification. The experiments carried out studied the accuracy and training behaviour of these network architectures. The system was trained and evaluated on the best-known database in this field, the NCUT, which contains low resolution, low contrast images. Therefore, different pre-processing steps were required, leading us to investigate the influence of a series of image quality enhancement methods such as Gaussian smoothing, inhomogeneity correction, contrast limited adaptive histogram equalization, ordinal image encoding, and coarse vein segmentation based on geometricalconsiderations. The results show high recognition accuracy for almost every such CNN-based setup