Weed detection utilizing quadratic polynomial and ROI techniques

Machine vision for selective weeding or selective herbicide spraying relies substantially on the ability of the system to analyze weed images and process the extracted knowledge for decision making prior to implementing the identified control action. To control weed, different weed type would require different herbicide formulation. Consequently the weed must be identified and classified accordingly. In this work, weed images were classified as either broad or narrow weed type. A fundamental problem in weed image recognition using planar curve analysis is to detect curve. It is difficult to successfully extract curve from the image of weed edges since the appropriate scale to use for extraction is not known a priori As such, this paper considers a curve detection method based on the quadratic polynomial technique which include the use of the region-of-interests (ROI) technique. The ROI technique creates image subsets by selecting regions of the displayed image. The ROIs are typically used to extract statistics for image operations such as classification. As such, the objective of this paper is to present a novel application of curve detection feature extraction technique in weed classification

Development of a 4D digital phantom for Cone-Beam CT (CBCT) imaging on the Varian On-Board Imager (OBI)

Mitigating effects of respiratory motion during image guided radiotherapy (IGRT) is important especially during thoracic and abdomen scanning protocols such as cone-beam CT (CBCT) imaging. However, the lack of ‘ground-truth’ in validating new algorithms has always been a challenge. The objective of this study is to outline the development of a novel 4D digital phantom for simulation of respiratory motion effects during CBCT image reconstruction based on Varian On-Board Imager (OBI): Half-Fan (HF) operating mode geometry. A set of actual 4D Magnetic Resonance (MR) data was used to develop the digital phantom. Firstly, the MR data sequencewasextendedto mimic a standard CBCT imaging acquisition protocol. Then, the images were segmented into several organs of interest and assigned with respective CT attenuation values. Subsequently, 2D projections of the developed digital phantom were simulated using the Varian OBI geometry. A Poisson noise model was also incorporated to the projection data to realistically simulate quantum noise that is present in an actual clinical environment. Three types of projections were then reconstructed using the standard 3D Feldkamp-Davis-Kress (FDK) algorithm, projections: without noise, with noise, and with noise and reconstructed with an additional Hann filter. As validation, the reconstructed images were compared against a single-frame of the developed phantom; quantitatively, using normalized root mean squared error (NRMSE) and qualitatively, using difference images. The results indicated that the phantom managed to display a consistent trend in modeling the effects of respiratory motion on the reconstructed images. On average, the NRMSE values for all three reconstructed images within the entire field-of-view (FOV) were evaluated to be approximately 29.07±0.22%. Nonetheless, the difference images indicated a large error in areas largely affected by respiratory motion. The NRMSE of a region-of-interest (ROI) near the affected area was evaluated as 51.26% that constitute to a significant +22.19% difference

Evaluation Methodology for Respiratory Signal Extraction from Clinical Cone-Beam CT (CBCT) using Data-Driven Methods

The absence of a ground truth for internal motion in clinical studies has always been a challenge to evaluate developed methods to extract respiratory motion especially during a 60-second cone-beam CT (CBCT) scan in Image-Guided Radiotherapy Treatment (IGRT). The unavailability of a gold standard has led this study to present a methodology to manually track respiratory motion on a clinically acquired CBCT projection data set over a 360° view angle. The tracked signal is then used as a reference to assess the performance of four data-driven methods in respiratory motion extraction, namely: the Amsterdam Shroud (AS), Local Principal Component Analysis (LPCA), Intensity Analysis (IA), and Fourier Transform (FT)-based methods that do not require additional equipment nor protocol to the existing treatment delivery. The assessment using this reference signal includes both quantitative and qualitative analysis. It is found out quantitatively that all four methods managed to extract respiratory signals that are highly correlated with the reference signal, with the LPCA method displaying the highest correlation coefficient value at 0.9108. Furthermore, the normalized root-mean-squared amplitude error of detected peaks and troughs within the signal from the LPCA method is also lowest at 1.6529 % compared to the other methods. This result is further supported by qualitative analysis via visual inspection of each extracted signal plotted with the reference signal on the same axes

Lung segmentation in CT for thoracic PET-CT registration through visual study

Hybrid Positron Emission Tomography (PET)/Computer Tomography (CT) is a combined device that merges the anatomical and functional information of a patient used most commonly in cancer diagnosis and staging. This device is said to improve co-registration of both the anatomical and functional information. However, in real clinical practices, voluntary and involuntary patient motions are inevitable during scanning resulting in registration errors. In our study, feature based registration algorithm is to be used to solve such problem focusing on the thoracic region. Thus, segmentation method is chosen as a preliminary step to segment significant anatomical regions such as the lungs. Lungs segmentation in CT image is implemented based on optimal thresholding, region growing, connected component labeling as well as morphological operations. Satisfactory lung boundary segmentation results are obtained through visual inspection for our current dataset; that are deemed agreeable to devise a solution for PET-CT thoracic misregistration

Newly Developed Nonlinear Vehicle Model for an Active Anti-roll Bar System

This paper presents the development of a newly developed nonlinear vehicle model is used in the validation process of the vehicle model. The parameters chosen in a newly developed vehicle model is developed based on CARSIM vehicle model by using non-dominated sorting genetic algorithm version II (NSGA-II) optimization method. The ride comfort and handling performances have been one of the main objective to fulfil the expectation of customers in the vehicle development. Full nonlinear vehicle model which consists of ride, handling and Magic tyre subsystems has been derived and developed in MATLAB/Simulink. Then, optimum values of the full nonlinear vehicle parameters are investigated by using NSGA-II. The two objective functions are established based on RMS error between simulation and benchmark system. A stiffer suspension provides good stability and handling during manoeuvres while softer suspension gives better ride quality. The final results indicated that the newly developed nonlinear vehicle model is behaving accurately with input ride and manoeuvre. The outputs trend are successfully replicated

A case study of improved PEO assessments in the electrical, electronic and systems engineering program at Universiti Kebangsaan Malaysia

Program educational objectives (PEOs) encompass a collection of statements that outline the knowledge, proficiencies, and competencies that graduates of a program should possess. PEO assessments are conducted for the purpose of evaluating the degree to which students have attained these objectives. However, the current assessment methods are not comprehensive and do not provide sufficient data to determine whether graduates are achieving the intended outcomes of the program. As a result, the program may not have sufficient data to provide informed decisions about curriculum development and student support services. This study provides an examination of the recently implemented Program Educational Objectives (PEO) evaluations within the undergraduate program of Electrical, Electronic and Systems Engineering, at the Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia (UKM). This study examines the difficulties encountered during the development and execution of direct and indirect PEO evaluations, along with the measures undertaken to enhance these assessments. It also includes a quantitative analysis of the findings generated from the newly implemented PEO assessment questionnaires. The study concludes by providing a discussion of the advantages associated with enhancing PEO assessments, as well as the potential usage for other engineering programs within the faculty

Ocular dimensions by three-dimensional magnetic resonance imaging in emmetropic versus myopic school children

Background: Magnetic resonance imaging (MRI) has been used to investigate eye shapes; however, reports involving children are scarce. This study aimed to determine ocular dimensions, and their correlations with refractive error, using three-dimensional MRI in emmetropic versus myopic children. Methods: Healthy school children aged < 10 years were invited to take part in this cross-sectional study. Refraction and best-corrected distance visual acuity (BCDVA) were determined using cycloplegic refraction and a logarithm of the minimum angle of resolution (logMAR) chart, respectively. All children underwent MRI using a 3-Tesla whole-body scanner. Quantitative eyeball measurements included the longitudinal axial length (LAL), horizontal width (HW), and vertical height (VH) along the cardinal axes. Correlation analysis was used to determine the association between the level of refractive error and the eyeball dimensions. Results: A total of 70 eyes from 70 children (35 male, 35 female) with a mean (standard deviation [SD]) age of 8.38 (0.49) years were included and analyzed. Mean (SD) refraction (spherical equivalent, SEQ) and BCDVA were -2.55 (1.45) D and -0.01 (0.06) logMAR, respectively. Ocular dimensions were greater in myopes than in emmetropes (all P < 0.05), with no significant differences according to sex. Mean (SD) ocular dimensions were LAL 24.07 (0.91) mm, HW 23.41 (0.82) mm, and VH 23.70 (0.88) mm for myopes, and LAL 22.69 (0.55) mm, HW 22.65 (0.63) mm, and VH 22.94 (0.69) mm for emmetropes. Significant correlations were noted between SEQ and ocular dimensions, with a greater change in LAL (0.46 mm/D, P < 0.001) than in VH (0.27 mm/D, P < 0.001) and HW (0.22 mm/D, P = 0.001). Conclusions: Myopic eyeballs are larger than those with emmetropia. The eyeball elongates as myopia increases, with the greatest change in LAL, the least in HW, and an intermediate change in VH. These changes manifest in both sexes at a young age and low level of myopia. These data may serve as a reference for monitoring the development of refractive error in young Malaysian children of Chinese origin

Improvement of thoracic hybrid PET/CT registration using hybrid feature with combined intensity multimodal demon with PET sinogram filtering

Accurately registered and fused PET/CT images are required for better tumor interpretation and the following tumor management in oncology and radiotherapy purposes. Although the hybrid PET/CT machine is supposedly solves the problem of misregistration between the PET/CT images, the offered solution is not optimal. The nonlinear misregistration due to physical and physiological motions stays on, declining the performance of the hybrid PET/CT machine. Therefore, the aim of this thesis is to solve the misregistration problem inflicting the PET/CT images acquired from the hybrid PET/CT scanner. Overall, the proposed registration method consists of three major steps. The first step is to perform 3D hybrid mean-median filtering based on the weighted average scheme on the PET sinogram domain. The second step is to segment selected structures of the thorax region which are the lungs, the heart and the tumor in both PET/CT images using a specific segmentation method for each structure excluding the heart in the PET image in which the segmentation is manually done. The main focus at this part is to design segmentation methods for the PET lung and the CT heart as these two subjects are rarely addressed. These segmented structures are used as “features” in the third stage where hybrid feature combined intensity multimodal demon registration is carried out to register both images. This method which is an improved version of multimodal demon registration uses a combination of mutual information (MI), sum of conditional variations (SCV) and multimodality independent neighborhood descriptive (MIND) similarity measures. The PET sinogram filter is tested on the NCAT based PET sinograms generated using ASIM PET simulator of different signal to noise ratio (SNR) and is compared with standard filter as used in analytical filtered-backprojection (FBP) reconstruction method. Aside from FBP, the improvement made by the filter on the iterative maximum likelihood expectation maximization with median root prior (MRP-MLEM) reconstruction method is also investigated. The filter significantly improves the global and local SNR of the PET image by more than 40% and more than 150% when compared to Hanning filtered FBP and MRP-MLEM reconstructed images without filtering. In terms of contrast to noise ratio (CNR), the proposed filter constantly generates improved CNR for all datasets in both analytical and statistical reconstruction methods. In the second stage, the proposed segmentation methods are evaluated on simulated NCAT based PET/CT and 21 clinical patient datasets. Apart from satisfactory subjective evaluation through visual displays,the segmentation of two structures, CT heart and PET lung are validated against expert segmentation on 10 datasets. The achieved mean Dice and Jaccard coefficients for both structures are more than 0.8. Then, the proposed improved intensity multimodal demon registration is tested on simple images and various types of medical images and the registration results are satisfactory. Specific to PET/CT registration problem, the proposed hybrid feature intensity multimodal registration method is tested on the simulated NCAT PET/CT images acquired at different breathing phases as well 21 clinical hybrid PET/CT datasets. Experimental results show that the combination of SCV and MIND based similarity measures produces the best registration result for PET/CT misregistration problem. In particular to clinical datasets experiment, the mean NMI improvement achieved by the proposed hybrid feature combined intensity multimodal demon registration is twice than the established free form deformation (FFD) registration method. The success of the registration of the patient datasets is also validated through improved lung volume overlap between the PET lung and the CT lung post registration according to Jaccard and Dice coefficients calculations. The registration method increases the Jaccard and Dice measures by 7.78% and 4.46% in average respectively after registration

Keberkesanan kaedah pengukuran dan penilaian hasil pembelajaran- hasil program (CO-PO)

Pembelajaran Berasaskan Hasil (PBH) merupakan satu pendekatan menyeluruh bagi mengurus dan mengendalikan proses pengajaran dan pembelajaran yang fokusnya adalah untuk menghasilkan pelajar yang berjaya mendemonstrasikan hasil pembelajaran dengan berkesan. Penilaian dan pengukuran ialah dua aspek penting dan dititikberatkan dalam PBH bagi memastikan hasil pembelajaran kursus (CO) dan seterusnya hasil program (PO) dicapai. Justeru, satu kaedah yang menggabungkan dua kaedah penilaian langsung dan tidak langsung telah dibangunkan untuk tujuan tersebut. Kaedah tidak langsung dikenali sebagai Penilaian Kendiri Pelajar (PKP) dan kaedah langsung merupakan kaedah penilaian summatif yang menggunakan gred akhir (GA). PKP adalah sebuah instrumen untuk menilai pencapaian pelajar secara kendiri berdasarkan sasaran CO yang telah digariskan dalam setiap kursus. Di Jabatan Kejuruteraan Elektrik, Elektronik dan Sistem, PKP telah digunakan untuk mengukur pencapaian CO secara tidak langsung dan seterusnya dikaitkan dengan pencapaian hasil program (PO). Kertas kerja ini bertujuan mengkaji keberkesanan instrumen PKP serta pencapaian GA sebagai kaedah mengukur pencapaian CO-PO. Tujuh kursus yang ditawarkan pada Semester 2 Sesi 2008/2009 telah dipilih secara rawak untuk tujuan kajian. Analisis maklum balas PKP serta perbandingan dengan GA menunjukkan kaedah ini adalah kurang sesuai untuk mengukur dan menilai pencapaian CO-PO. Walau bagaimanapun, gabungan kedua-duanya didapati sesuai untuk memantau dan menilai pengendalian sesuatu kursus di mana perbezaan besar (>10%) antara keputusan PKP akhir dengan GA boleh dijadikan sebagai petunjuk keperluan melakukan penambahbaikan terhadap pengendalian kursus tersebut. Kesimpulannya, satu penambahbaikan diperlukan untuk memastikan CO-PO dapat diukur dan dinilai secara lebih objektif dan langsung

Using equine assisted learning to boost character skills and academic achievement among university students

Recreational horseback riding has been recognized for its therapeutic benefits, particularly through direct human-horse interactions. Despite known positive impacts of horse’s interactions with humans, there is no reported study discussing the relationship of equine assisted therapy and character skill development among university students. This paper examines the impact of equine assisted learning (EAL) on university students who struggled academically in the previous semester. Twelve students participated voluntarily in the program, engaging the study participants in experiential learning of EAL sessions at Majlis Ekuin Malaysia horse stable. The learning sessions, each conducted for nearly three hours, involved tasks to complete including stable cleaning, grooming, feeding, and leading horses. Study participants were requested to complete questionnaires before and after the EAL program to assess their character skill development, measured through "Habits of Minds." Students’ academic results in terms of grade point average (GPA) achieved in the previous semester before EAL were compared with their GPA for the current semester after the students had been involved in the EAL program. Results of the study revealed collective improvements in the “Habits of Minds'' attributes of Persistence, Flexible Thinking, Responsible Risk-Taking, and Empathetic Listening after the EAL program. However, the attribute Managing Impulsivity was slightly impaired. Interestingly, all students’ GPA after the EAL program had also improved compared to their previous GPA. The (mean + standard deviation) of the students’ GPA after EAL intervention (2.21 + 0.59) is significantly increased compared to that before the EAL intervention (1.66 + 0.67) with p = 0.045. The overall finding from this study suggests that the practical benefits of the EAL program can enhance character skills development that can lead to better academic results among university students