IMPROVEMENT OF HANDWRITING JAVASCRAFT IMAGE QUALITY AND SEGMENTATION WITH CLOSING MORPHOLOGY AND ADAPTIVE THRESHOLDING METHODS

Tujuan: Perbaikan kualitas citra yang putus-putus atau terlalu tipis pada aksara jawa tulisan tangan menggunakan operasi morfologi dan mengumpulkan dataset secara otomatis dari proses cropping dengan metode Connected Component Labeling.Perancangan/metode/pendekatan: Menerapkan metode operasi morfologi dalam perbaikan citra putus-putus dan metode connected component labeling untuk membantu cropping dalam mengumpulkan dataset secara otomatis.Hasil: Hasil uji coba dengan beberapa kernel yang berbeda antara operasi morfologi opening dan operasi morfologi closing terpilih operasi morfologi closing dengan kernel (45,45) pada bagian dilasi dan kernel (37,37) pada bagian erosi. Hasil dari segmentasi yang terpilih lanjut ke cropping dengan bantuan metode connected component labeling dan klasifikasi convolutional neural network yang diterapkan untuk mengklasifikasi citra aksara jawa dengan baik. Akurasi yang diperoleh adalah sebesar 94,27 % pada proses klasifikasi menggunakan data training dan akurasi 84,53% pada proses klasifikasi menggunakan data validasi.Keaslian/ state of the art: Pengujian dari operasi morfologi opening dan operasi morfologi closing dengan masing-masing 6 kernel berbeda pada proses segmentasi citra aksara jawa untuk perbaikan kualitas citra. Pengumpulan dataset secara otomatis dari hasil cropping citra dengan bantuan metode connected component labeling dan hasil dataset yang terkumpul diklasifikasi untuk masing-masing citra aksara jawa

Determining appropriate imaging parameters for kilovoltage intrafraction monitoring: an experimental phantom study.

Kilovoltage intrafraction monitoring (KIM) utilises the kV imager during treatment for real-time tracking of prostate fiducial markers. However, its effectiveness relies on sufficient image quality for the fiducial tracking task. To guide the performance characterisation of KIM under different clinically relevant conditions, the effect of different kV parameters and patient size on image quality, and quantification of MV scatter from the patient to the kV detector panel were investigated in this study. Image quality was determined for a range of kV acquisition frame rates, kV exposure, MV dose rates and patient sizes. Two methods were used to determine image quality; the ratio of kV signal through the patient to the MV scatter from the patient incident on the kilovoltage detector, and the signal-to-noise ratio (SNR). The effect of patient size and frame rate on MV scatter was evaluated in a homogeneous CIRS pelvis phantom and marker segmentation was determined utilising the Rando phantom with embedded markers. MV scatter incident on the detector was shown to be dependent on patient thickness and frame rate. The segmentation code was shown to be successful for all frame rates above 3 Hz for the Rando phantom corresponding to a kV to MV ratio of 0.16 and an SNR of 1.67. For a maximum patient dimension less than 36.4 cm the conservative kV parameters of 5 Hz at 1 mAs can be used to reduce dose while retaining image quality, where the current baseline kV parameters of 10 Hz at 1 mAs is shown to be adequate for marker segmentation up to a patient dimension of 40 cm. In conclusion, the MV scatter component of image quality noise for KIM has been quantified. For most prostate patients, use of KIM with 10 Hz imaging at 1 mAs is adequate however image quality can be maintained and imaging dose reduced by altering existing acquisition parameters

Determining appropriate imaging parameters for kilovoltage intrafraction monitoring: an experimental phantom study.

Kilovoltage intrafraction monitoring (KIM) utilises the kV imager during treatment for real-time tracking of prostate fiducial markers. However, its effectiveness relies on sufficient image quality for the fiducial tracking task. To guide the performance characterisation of KIM under different clinically relevant conditions, the effect of different kV parameters and patient size on image quality, and quantification of MV scatter from the patient to the kV detector panel were investigated in this study. Image quality was determined for a range of kV acquisition frame rates, kV exposure, MV dose rates and patient sizes. Two methods were used to determine image quality; the ratio of kV signal through the patient to the MV scatter from the patient incident on the kilovoltage detector, and the signal-to-noise ratio (SNR). The effect of patient size and frame rate on MV scatter was evaluated in a homogeneous CIRS pelvis phantom and marker segmentation was determined utilising the Rando phantom with embedded markers. MV scatter incident on the detector was shown to be dependent on patient thickness and frame rate. The segmentation code was shown to be successful for all frame rates above 3 Hz for the Rando phantom corresponding to a kV to MV ratio of 0.16 and an SNR of 1.67. For a maximum patient dimension less than 36.4 cm the conservative kV parameters of 5 Hz at 1 mAs can be used to reduce dose while retaining image quality, where the current baseline kV parameters of 10 Hz at 1 mAs is shown to be adequate for marker segmentation up to a patient dimension of 40 cm. In conclusion, the MV scatter component of image quality noise for KIM has been quantified. For most prostate patients, use of KIM with 10 Hz imaging at 1 mAs is adequate however image quality can be maintained and imaging dose reduced by altering existing acquisition parameters

395 OSTEOPHYTES AND JOINT SPACE NARROWING ARE INDEPENDENTLY ASSOCIATED WITH PAIN IN FINGER JOINTS IN HAND OSTEOARTHRITIS

Objective To study the associations between structural abnormalities on ultrasound (US) or conventional x-rays (CR) and pain in hand osteoarthritis (HOA). Methods In 55 consecutive patients with HOA (mean age 61 years, 86% women) fulfilling the American College of Rheumatology criteria, pain in 30 separate hand joints was assessed upon palpation; osteophytes were assessed by US and CR and joint space narrowing (JSN) by CR. Associations between structural abnormalities and pain per joint were analysed using generalised estimated equations to account for patient effects and adjusted for age, sex, body mass index, US inflammatory features and other remaining structural abnormalities. Results In 1649 joints, 69% and 46% had osteophytes on US and CR, respectively and 47% had JSN. Osteophytes and JSN showed independent associations with pain per joint adjusted: OR for osteophytes: 4.8 (95% CI 3.1 to 7.5) for US and 4.1 (95% CI 2.4 to 7.1) for CR; for JSN: 4.2 (95% CI 2.0 to 9.0). Conclusions Osteophytes and JSN are independently associated with pain in individual HOA joints, taking into account patient effects

Polyp-DDPM: Diffusion-Based Semantic Polyp Synthesis for Enhanced Segmentation

This study introduces Polyp-DDPM, a diffusion-based method for generating realistic images of polyps conditioned on masks, aimed at enhancing the segmentation of gastrointestinal (GI) tract polyps. Our approach addresses the challenges of data limitations, high annotation costs, and privacy concerns associated with medical images. By conditioning the diffusion model on segmentation masks-binary masks that represent abnormal areas-Polyp-DDPM outperforms state-of-the-art methods in terms of image quality (achieving a Frechet Inception Distance (FID) score of 78.47, compared to scores above 83.79) and segmentation performance (achieving an Intersection over Union (IoU) of 0.7156, versus less than 0.6694 for synthetic images from baseline models and 0.7067 for real data). Our method generates a high-quality, diverse synthetic dataset for training, thereby enhancing polyp segmentation models to be comparable with real images and offering greater data augmentation capabilities to improve segmentation models. The source code and pretrained weights for Polyp-DDPM are made publicly available at https://github.com/mobaidoctor/polyp-ddpm.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Automating Cobb Angle Measurement for Adolescent Idiopathic Scoliosis using Instance Segmentation

Scoliosis is a three-dimensional deformity of the spine, most often diagnosed in childhood. It affects 2-3% of the population, which is approximately seven million people in North America. Currently, the reference standard for assessing scoliosis is based on the manual assignment of Cobb angles at the site of the curvature center. This manual process is time consuming and unreliable as it is affected by inter- and intra-observer variance. To overcome these inaccuracies, machine learning (ML) methods can be used to automate the Cobb angle measurement process. This paper proposes to address the Cobb angle measurement task using YOLACT, an instance segmentation model. The proposed method first segments the vertebrae in an X-Ray image using YOLACT, then it tracks the important landmarks using the minimum bounding box approach. Lastly, the extracted landmarks are used to calculate the corresponding Cobb angles. The model achieved a Symmetric Mean Absolute Percentage Error (SMAPE) score of 10.76%, demonstrating the reliability of this process in both vertebra localization and Cobb angle measurement