OBJECTIVETrauma in India is an increasingly significant problem, particularly in light of rapid development and increasing motorization. Social changes are resulting in alterations in the epidemiology of trauma. The aim of the study was to assess the various epidemiological parameters that influence the cause of injury in the patients admitted to a major trauma centre in northern India.

METHODSAn observational study of 748 patients chosen by random assortment was carried out over a period of 1 year (August 2008 to July 2009). Age, sex, injury type and pattern were noted. Injury mode of upper and lower limbs was also noted.

RESULTSInjuries occur predominately in the age group of 15-30 years. Males incurred more injury with male to female ratio of 6:1. The most vulnerable group was motorcycle users. Among the injured, farmers were the most commonly involved. Blunt injuries (94.92%) were much more common than penetrating injuries. Among patients with head injury, two-wheeler related accidents were the most common (40.3%). Most spinal cord injuries were caused by falls from height (51.09%). Most lower limb fractures were simple type. Compound fractures of the lower limb were more common than upper limb fractures.

CONCLUSIONStrict enforcement of traffic rules, combined with improved infrastructure and behavior change can decrease the burden of road traffic accidents in India and other developing countries. This study could assist in raising the profile of road traffic accidents as a public health problem which needs to be addressed as a preventable cause of mortality and morbidity, and planning appropriate interventions for this major challenge. Preventive strategies should be made on the basis of these epidemiological trends.

Accidents, Traffic ; statistics & numerical data ; Adolescent ; Adult ; Aged ; Craniocerebral Trauma ; epidemiology ; Female ; Humans ; India ; epidemiology ; Male ; Middle Aged ; Spinal Cord Injuries ; epidemiology ; Wounds and Injuries ; epidemiology

Mammogram images are majorly used for detecting the breast cancer. The level of positivity of breast cancer is detected after excluding the pectoral muscle from mammogram images. Hence, it is very significant to identify and segment the pectoral muscle from the mammographic images. In this work, a new multilevel thresholding, on the basis of electro-magnetism optimization (EMO) technique, is proposed. The EMO works on the principle of attractive and repulsive forces among the charges to develop the members of a population. Here, both Kapur's and Otsu based cost functions are employed with EMO separately. These standard functions are executed over the EMO operator till the best solution is achieved. Thus, optimal threshold levels can be identified for the considered mammographic image. The proposed methodology is applied on all the three twenty-two mammogram images available in mammographic image analysis society dataset, and successful segmentation of the pectoral muscle is achieved for majority of the mammogram images. Hence, the proposed algorithm is found to be robust for variations in the pectoral muscle.
Breast Neoplasms ; Dataset