Lung X-Ray Segmentation using Quadrant-Based Tracing Method

Chest X-Ray is one of the most popular imaging modalities. Chest X-ray has been a subject of various imaging-related research for years. Among the various research, Lung segmentation is one of the most prominent ones.  Nowadays the trend of research in segmentation is moving toward deep learning however traditional segmentation has advantage of requiring less calculation resources thus still has potential to be explored. In this paper an alternative non-deep learning segmentation method using graph-based method to trace border of the Chest X-Ray lung region is proposed. Chest X-Ray image was treated as a graph with coordinate of the pixels as vertex and value of the pixels as edges. First the image was divided into 4 quadrants, then the border of lung region on each quadrant was traced by finding the minimum spanning tree of the graphs on each quadrant, then the pixels recorded as the tree was smoothed and optimized using Savitzky-Golay filter. The results were analyzed using the confusion matrix by comparing the proposed method results with manual segmentation by a radiologist. The proposed method is successfully segment lung area on lateral view of chest X-Ray with an average accuracy of 0.936. Two sample T-test also employed in order to show that there is no significant difference between the proposed method results and manual segmentation by radiologist

Automatic segmentation of skin cells in multiphoton data using multi-stage merging

We propose a novel automatic segmentation algorithm that separates the components of human skin cells from the rest of the tissue in fluorescence data of three-dimensional scans using non-invasive multiphoton tomography. The algorithm encompasses a multi-stage merging on preprocessed superpixel images to ensure independence from a single empirical global threshold. This leads to a high robustness of the segmentation considering the depth-dependent data characteristics, which include variable contrasts and cell sizes. The subsequent classification of cell cytoplasm and nuclei are based on a cell model described by a set of four features. Two novel features, a relationship between outer cell and inner nucleus (OCIN) and a stability index, were derived. The OCIN feature describes the topology of the model, while the stability index indicates segment quality in the multi-stage merging process. These two new features, combined with the local gradient magnitude and compactness, are used for the model-based fuzzy evaluation of the cell segments. We exemplify our approach on an image stack with 200 × 200 × 100 μm3, including the skin layers of the stratum spinosum and the stratum basale of a healthy volunteer. Our image processing pipeline contributes to the fully automated classification of human skin cells in multiphoton data and provides a basis for the detection of skin cancer using non-invasive optical biopsy

A Novel Design of Needle Aspiration Biopsy Monitoring Instrument (NAOMI) Tested on a Low Cost Chest Phantom

Needle biopsy is a medical intervention method for taking a lung tissue sample that suspected as a cancer. The disadvantage is the physicians directly visualize the anatomical structures in an open surgery for lung cancer biopsy procedure. There is a need to develop an instrument that may help the physician to guarantee the accuracy and efficiency while performing needle aspiration biopsy. Therefore, a needle aspiration biopsy monitoring instrument or named as NAOMI is proposed. It consists of a microcontroller system, an IMU sensor, an ultrasonic ranging module, a bluetooth module, and a 9V lithium battery. The experimental testing consist of performance testing, functional testing using chest phantom, and user acceptances. The results showed that the NAOMI improve the accuracy and efficiency while performing the needle biopsy operation

A Novel Design of Needle Aspiration Biopsy Monitoring Instrument (NAOMI) Tested on a Low Cost Chest Phantom

Needle biopsy is a medical intervention method for taking a lung tissue sample that suspected as a cancer. The disadvantage is the physicians directly visualize the anatomical structures in an open surgery for lung cancer biopsy procedure. There is a need to develop an instrument that may help the physician to guarantee the accuracy and efficiency while performing needle aspiration biopsy. Therefore, a needle aspiration biopsy monitoring instrument or named as NAOMI is proposed. It consists of a microcontroller system, an IMU sensor, an ultrasonic ranging module, a bluetooth module, and a 9V lithium battery. The experimental testing consist of performance testing, functional testing using chest phantom, and user acceptances. The results showed that the NAOMI improve the accuracy and efficiency while performing the needle biopsy operation

Community childhood obesity assessment in elementary school, anthropometric indices as screening tools: a community cross-sectional study in Indonesia

Background Representative anthropometric epidemiological data are needed to formulate screening and intervention methods to prevent obesity in children. This study aims to conduct community childhood obesity assessment in elementary school based on anthropometric measurements and evaluate its predictive value.Methods This cross-sectional study was carried out in Palembang, Indonesia, and involved 1180 elementary school students. The anthropometric parameters were divided into (1) basic data: stature, weight and waist circumference (WC), hip circumference (HC); (2) structural dimensions: the segmental dimensions of head-neck, trunk, upper extremity, hand, lower extremity and foot and (3) postural dimensions: the relative spacial dimensions when standing. Six anthropometric indices were considered: body mass index, waist-to-hip ratio (WHR), waist-to-height ratio (WHtR), Conicity Index (CI), Body Adiposity Index (BAI) and Tri-ponderal Mass Index (TMI).Results The proportion of overweight and obesity was 50.17% (n=592) and normal weight was 49.83% (n=588). The mean age was 8.26±1.71 years. The averages of all measured indices in overweight/obese versus normal weight were significant difference among boys and girls in height, weight, WC, HC, neck circumference, WHR, WHtR, neck-to-height ratio, BAI, TMI and CI (p<0.05 for all). TMI was the best predictor of obesity based on area under the curve (AUC) values, both in boys (sensitivity=90.48; specificity=91.53; AUC=0.975) and in girls (sensitivity=90.28; specificity=90.00; AUC=0.968).Conclusions A trustworthy anthropometric database of primary school students might be a helpful local resource when working on projects involving children. In order to improve the quality of life through better-suited and secure products and environmental designs, it is crucial to build an anthropometric database