In order to assess the feasibility of using the measurement of tissue hardness as a method of diagnosing compartment syndrome noninvasively in children, a simple hand-held device to measure tissue hardness was fabricated. The relationship between hardness and compartmental pressure was studied in an experimental model and in three fresh amputated lower limbs. Normal tissue hardness of the forearm was measured in 189 children and 20 adults to identify the factors that influence normal tissue hardness. The reproducibility of measurement of tissue hardness was assessed on the experimental model, on the amputated limbs and in normal individuals. Experimental data from this study suggest that there is a nonlinear relationship between intracompartmental pressure and tissue hardness. The study also shows that tissue hardness can be measured reproducibly in the forearm of children with the device. Several factors influence tissue hardness such as the age of the child, the site of measurement on the limb, the hand dominance and active muscle contraction. These factors may affect the specificity of this measure as a screening tool for diagnosing compartment syndrome. Further refinement of the measuring device and well designed clinical trials are needed to establish whether compartmental syndrome can be diagnosed reliably by measuring tissue hardness noninvasively.

We studied the natural history of Perthes' disease in 62 children in whom the onset of symptoms was in adolescence.Three patterns of disease were noted, namely, late-onset pattern, segmental collapse, or destructive with failure of revascularisation. In the late-onset pattern, the disease followed the sequence of healing seen in younger children, but adequate epiphyseal remodelling did not occur. Consequently, the femoral head was never spherical after revascularisation. With segmental collapse, early and irreversible collapse of part of the epiphysis occurred with gross deformation of the femoral head. The destructive pattern was characterised by a failure of revascularisation and repair of the avascular epiphysis.The radiological outcome was poor in all three patterns. The poorest clinical results were found in the destructive type which was frequently associated with incapacitating pain requiring arthrodesis or excision arthroplasty within three years of onset of the disease. J Bone Joint Surg [Br] 2001;83-B:715-20.

A displaced transcervical fracture of the femoral neck in a three-year-eight-month-old boy was fixed with two screws, which did not cross the growth plate. When he resumed walking five weeks after the injury, a delayed separation of the capital femoral epiphysis occurred. The displaced epiphysis was reduced and fixed with three unthreaded pins. In spite of disruption of the femoral neck at two sites, avascular necrosis of the femoral head did not occur. This was confirmed by two sequential isotope scans. Delayed epiphyseal separation after the femoral neck fracture and the preservation of the vascularity of the epiphysis in this case are both very unusual.

The main contribution of this article is introducing an intelligent classifier to distinguish between benign and malignant areas of micro-calcification in companded mammogram image which is not proved or addressed elsewhere. This method does not require any manual processing technique for classification, thus it can be assimilated for identifying benign and malignant areas in intelligent way. Moreover it gives good classification responses for compressed mammogram image. The goal of the proposed method is twofold: one is to preserve the details in Region of Interest (ROI) at low bit rate without affecting the diagnostic related information and second is to classify and segment the micro-calcification area in reconstructed mammogram image with high accuracy. The prime contribution of this work is that details of ROI and Non-ROI regions extracted using multi-wavelet transform are coded at variable bit rate using proposed Region Based Set Partitioning in Hierarchical Trees (RBSPIHT) before storing or transmitting the image. Image reconstructed during retrieval or at the receiving end is preprocessed to remove the channel noise and to enhance the diagnostic contrast information. Then the preprocessed image is classified as normal or abnormal (benign or malignant) using Probabilistic neural network. Segmentation of cancerous region is done using Fuzzy C-means Clustering (FCC) algorithm and the cancerous area is computed. The experimental result shows that the proposed model performance is good at achieving high sensitivity of 97.27%, specificity of 94.38% at an average compression rate and Peak Signal to Noise Ratio (PSNR) of 0.5bpp and 58dB respectively.

Surgeons can use this model to effectively teach trainees in this field about the mechanics and anatomy of the subtalar joint and other relevant applications.