In this paper, we review the state-of-the-art approaches for knee articular cartilage segmentation from conventional techniques to deep learning (DL) based techniques. Knee articular cartilage segmentation on magnetic resonance (MR) images is of great importance in early diagnosis of osteoarthritis (OA). Besides, segmentation allows estimating the articular cartilage loss rate which is utilised in clinical practice for assessing the disease progression and morphological changes. It has been traditionally applied in quantifying longitudinal knee OA progression pattern to detect and assess the articular cartilage thickness and volume. Topics covered include various image processing algorithms and major features of different segmentation techniques, feature computations and the performance evaluation metrics. This paper is intended to provide researchers with a broad overview of the currently existing methods in the field, as well as to highlight the shortcomings and potential considerations in the application at clinical practice. The survey showed that state-of-the-art techniques based on DL outperform the other segmentation methods. The analysis of the existing methods reveals that integration of DL-based algorithms with other traditional model-based approaches has achieved the best results (mean Dice similarity coefficient (DSC) between 85.8% and 90%).

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Automatic face identification and verification from facial images attain good accuracy with large sets of training data while face attribute recognition from facial images still remain challengeable. Hence introducing an efficient and accurate facial image classification based on facial attributes is an important task. This paper proposes a methodology for automatic age and gender classification based on feature extraction from facial images. In contrast to the other mechanisms proposed in the literature, the main concern of this methodology is the use of biometric feature variation of male and female for the classification. It uses two types of features namely, primary and secondary features and it includes three main iterations: Preprocessing, Feature extraction and Classification. This study has been carried out using facial images of age range 8-60 years consisting of both gender types and the age classification has been done according to predefined age ranges. Proposed solution is able to classify images in different lighting conditions and different illumination conditions. Classification is done using Artificial Neural Networks according to the different shape and texture variations of wrinkles on face images. This study has been evaluated and tested on both foreign and Asian face images in both gender types and the four age categories used.

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