Incidence of pneumothorax following mediastinal drain removal is very low. Clinical signs and symptoms almost always identify those few patients requiring intervention and the decision to obtain an X-ray could be based on clinical judgement alone. In addition, this approach may result in cost savings without compromising patient safety.

Knee is a complex and articulated joint of the body. Cartilage is a smooth hyaline spongy material between the tibia and femur bones of knee joint. Cartilage morphology change is an important biomarker for the progression of osteoarthritis (OA). Magnetic resonance imaging (MRI) is the modality widely used to image the knee joint because of its hazard free and high resolution soft tissue contrast. Cartilage thickness measurement and visualization is useful for early detection and progression of the disease in case of OA affected patients. A wide variety of algorithms are available for knee joint image segmentation. They are classified as pixel based and model based methods. Based on the human intervention required, segmentation methods are also classified as manual, semi-automatic and fully automatic methods. This paper reviews knee joint articular cartilage segmentation methods, visualization, thickness measurement, volume measurement and validation methods.

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The medial and lateral meniscus of the knee is a crescent shaped cartilage pad between the femur and the tibia bones of knee joint. The meniscus acts as a smooth surface for the joint to move on. It helps in distributing forces of weight on joint surfaces and provides stability by preventing lateral movement of the knee joint. In osteoarthritis (OA) knee joints menisci undergoes degeneration. In sports knee injuries result in menisci tears. The Magnetic resonance imaging (MRI) with low field strength (1.5-T) and high field strength (3.0-T) are used for diagnosis and treatment of menisci pathologies. Image processing techniques are used in visualization and detection of menisci tears. In the present work, knee MR Images in sagittal, coronal and axial views are processed for visualization of menisci and detection of menisci tears using thresholding and Canny edge detection approaches. The method segments femur, tibia, cartilage and menisci. Thickness of menisci is plotted and the processing steps are extended for detection of menisci tears.