Jelte Peter Vink

Efficient nucleus detector in histopathology images

In traditional cancer diagnosis, (histo)pathological images of biopsy samples are visually analysed by pathologists. However, this judgment is subjective and leads to variability among pathologists. Digital scanners may enable automated objective assessment, improved quality and reduced throughput time. Nucleus detection is seen as the corner stone for a range of applications in automated assessment of (histo)pathological images.

An automatic vision-based malaria diagnosis system.

Malaria is a worldwide health problem with 225 million infections each year. A fast and easy‐to‐use method, with high performance is required to differentiate malaria from non‐malarial fevers. Manual examination of blood smears is currently the gold standard, but it is time‐consuming, labour‐intensive, requires skilled microscopists and the sensitivity of the method depends heavily on the skills of the microscopist.

Comparison of machine learning techniques for target detection

This paper focuses on machine learning techniques for real-time detection. Although many supervised learning techniques have been described in the literature, no technique always performs best. Several comparative studies are available, but have not always been performed carefully, leading to invalid conclusions. Since benchmarking all techniques is a tremendous task, literature has been used to limit the available options, selecting the two most promising techniques (AdaBoost and SVM), out of 11 different Machine Learning techniques. Based on a thorough comparison using 2 datasets and simulating noise in the feature set as well as in the labeling, AdaBoost is concluded to be the best machine learning technique for real-time target detection as its performance is comparable to SVM, its detection time is one or multiple orders of magnitude faster, its inherent feature selection eliminates this as a separate task, while it is more straightforward to use (only three coupled parameters to tune) and has a lower training time.

No-Reference Metric Design With Machine Learning for Local Video Compression Artifact Level

In decoded digital video, the local perceptual compression artifact level depends on the global compression ratio and the local video content. In this paper, we show how to build a highly relevant metric for video compression artifacts using supervised learning. To obtain the ground truth for training, we first build a reference metric for local estimation of the artifact level, which is robust to scaling and sensitive to all types of compression artifacts. Next, we design a large feature set and use AdaBoost to create no-reference metrics trained with the output of the reference metric. Two separate trained no-reference metrics, one for flat and one for detailed areas, respectively, are necessary to cover all types of artifacts. The relevance of these metrics is validated in a compression artifact reduction application, using objective scores like PSNR and BIM, but also a subjective evaluation as proof. We conclude that our created reference metric is an accurate local estimator of the compression artifact level. We were able to copy the performance to two no-reference metrics, based on a weighted mixture of low-level features. Our new metrics enable a far superior performance of artifact reduction compared to relevant alternative proposals.

Robust and Sensitive Video Motion Detection for Sleep Analysis

In this paper, we propose a camera-based system combining video motion detection, motion estimation, and texture analysis with machine learning for sleep analysis. The system is robust to time-varying illumination conditions while using standard camera and infrared illumination hardware. We tested the system for periodic limb movement (PLM) detection during sleep, using EMG signals as a reference. We evaluated the motion detection performance both per frame and with respect to movement event classification relevant for PLM detection. The Matthews correlation coefficient improved by a factor of 2, compared to a state-of-the-art motion detection method, while sensitivity and specificity increased with 45% and 15%, respectively. Movement event classification improved by a factor of 6 and 3 in constant and highly varying lighting conditions, respectively. On 11 PLM patient test sequences, the proposed system achieved a 100% accurate PLM index (PLMI) score with a slight temporal misalignment of the starting time ( 1 s) regarding one movement. We conclude that camera-based PLM detection during sleep is feasible and can give an indication of the PLMI score.

Robust skin detection using multi-spectral illumination

In computer vision, many applications could greatly benefit from multi-spectral image data. Our aim is to illustrate the effectiveness of multi-spectral analysis obtained from a simple and cost-effective system. While the proposed approach is broadly applicable, in this paper we focus on the specific case of skin detection. To obtain the multi-spectral data, we have assembled a system using multiple LEDs with different spectra to illuminate the scene and a conventional RGB camera to acquire images. A methodology is proposed to avoid strict requirements on the experimental environment, by adopting a simple training procedure which is tuned for the detection of human skin. Next a specific feature set is defined and a corresponding normalization method is designed to improve the robustness to changes in skin color and incident light, issues not addressed by available prior art. Finally, we use supervised learning to train our skin detector. We demonstrate the accuracy and effectiveness of our skin detector through extensive benchmarking. The proposed methodology enables a superior performance of skin detection compared to relevant alternative proposals.

Local estimation of video compression artifacts

In decoded digital video, the local compression artifact level depends on the global compression ratio and the local video content. We describe an algorithm to create estimators of the local ringing and blocking artifact levels from the decoded video signal. Our estimators enable high quality video enhancement in consumer TV.