Patellar Tendinopathy (PT), commonly called "Jumper's Knee", is a condition resulting from repetitive loading of the patellar tendon that presents as anterior knee pain, which is commonly seen in basketball players due to the maneuvers in the sport. Diagnosis of PT often involves a clinical exam followed by ultrasound images for confirmation of the diagnosis to look for key factors of PT. Clinical assessment of ultrasound images of tendons is subjective and requires a high level of experience for reliable interpretation. Thus, there is a need for objective, quantitative methods to assess tendon abnormalities associated with pathology. Ultrasound image texture analysis has emerged as a reliable technique to augment the utility of conventional US imaging, and has recently been shown to distinguish healthy from abnormal tendon and myofascial tissues. The objective of the present study was to conduct image texture analysis to evaluate patellar tendons of collegiate basketball athletes over two seasons. Under an IRB-approved protocol with informed consent, a total of 33 Division 1 collegiate basketball athletes (16 male, 17 female, age 19.9 +/- 1.4 years) underwent clinical evaluation and ultrasound imaging. Four imaging sessions were collected over the course of two years (pre- and post-season). Participants were imaged using a GE LOGIQ S8 (General Electric, USA) ultrasound machine equipped with ML6-15 linear probe. At each imaging session, power Doppler images were collected in the longitudinal and transverse axis, at the proximal, central, and distal regions of the patellar tendon of both knees. Image texture analysis was performed using a custom MATLAB (Mathworks, USA) program to obtain first order (mean, median, variance, skewness, kurtosis, entropy), second order (contrast, energy, and homogeneity), and blob analysis (blob count, BC, and blob area, BA, for 5%, 25%, 50%, 75%, and 95% thresholding values) texture parameters in each image, based upon borders manually drawn by a single researcher. Statistical analysis was conducted to compare imaging sessions (JMP Pro 16, SAS). P-values <0.05 were considered statistically significant. Quantitative texture parameters are able to distinguish characteristics in patellar tendon ultrasound images to distinguish between anatomic region, gender, dominance and pre- to post- season. The 25% and 75% thresholding percentiles effectively showed characteristics of collagen fibers in the patellar tendon. The abnormal diagnosis does not greatly effect texture parameters, which needs to be investigated with more incorporation of grading criteria distinctions and a larger sample size. / Master of Science / Patellar Tendinopathy (PT) is a knee injury that commonly occurs in basketball players. The recovery for PT is often long and the player can still have knee pain when returning to the sport. Diagnosis of PT requires a high level of expertise to consider the patients history, conduct a physical exam and take ultrasound images to look for factors that indicate patellar tendon is damaged. The difficulty of diagnosing PT calls for an objective method to allow for accuracy in assessing patellar tendons. In order to create a more objective measure of ultrasound images, quantitative texture parameters are explored to understand what the brightness values of each pixel and the proximity of pixels together can convey about the image. The objective of this study is to understand what characteristics of the subject (anatomic region, knee dominance, gender, and time point) texture parameters are able to distinguish in patellar tendon ultrasound images.

Honeybees have a rich genetic diversity in Anatolia. This is reflected in the presence of numerous subspecies of honeybee in Turkey. In METU, Department of Biology, honeybee populations of different regions in Turkey are investigated in order to characterize population variation in these regions. A total of 23 length and angle features belonging to the honeybee hindlegs and forewings are measured in these studies using a microscope and a monitor. These measurements are carried out by placing rulers on the monitor that shows the honeybee image and getting the length and angle features. However, performing measurements in this way is a time consuming process and is open to human-dependent errors. In this thesis, a &ldquo / semi-automated honeybee feature extraction system&rdquo / is presented. The aim is to increase the efficiency by decreasing the time spent on handling these measurements and by increasing the accuracy of measured hindleg and forewing features. The problem is studied from the acquisition of the microscope images, to the feature extraction of the honeybee features. In this scope, suitable methods are developed for segmentation of honeybee hindleg and forewing images. Within intermediate steps, blob analysis is utilized, and edges of the forewing and hindlegs are thinned using skeletonization. Templates that represent the forewing and hindleg edges are formed by either Bezier Curves or Polynomial Interpolation. In the feature extraction phase, Active Contour (Snake) algorithm is applied to the images in order to find the critical points using these templates.

The diploma thesis deals with the usage of integrated machine vision by B&R, Smart Sensor, for metal rectangular-shaped workpiece recognition and position determinition. The description of the usage of machine vision in the industry is made, the solution concept is created and the program and the user visualisation implemented. The main outcome of this work is the VITemplate library which can be used in combination with the model-based Blob analysis implemented in Smart Sensor to control the robotic arm to successfully grab all the workpieces on the belt.