Browsing by Author "Monteiro, Fernando C."
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- An Evaluation of Image Preprocessing in Skin Lesions DetectionPublication . Silva, Giuliana Martins; Lazzaretti, André E.; Monteiro, Fernando C.This study aims to evaluate the impact of image preprocessing techniques on the performance of Convolutional Neural Networks (CNNs) in the task of skin lesion classification. The study is made on the ISIC 2017 dataset, a widely used resource in skin cancer diagnosis research. Thirteen popular CNN models were trained using transfer learning. An ensemble strategy was also employed to generate a final diagnosis based on the classifications of different models. The results indicate that image preprocessing can significantly enhance the performance of CNN models in skin lesion classification tasks. Our best model obtained a balanced accuracy of 0.7879.
- Angle assessment for upper limb rehabilitation: a novel light detection and ranging (LiDAR)-based approachPublication . Klein, Luan C.; Chellal, Arezki Abderrahim; Grilo, Vinicius F.S.B.; Braun, João; Gonçalves, José; Pacheco, Maria F.; Fernandes, Florbela P.; Monteiro, Fernando C.; Lima, JoséThe accurate measurement of joint angles during patient rehabilitation is crucial for informed decision making by physiotherapists. Presently, visual inspection stands as one of the prevalent methods for angle assessment. Although it could appear the most straightforward way to assess the angles, it presents a problem related to the high susceptibility to error in the angle estimation. In light of this, this study investigates the possibility of using a new approach to angle calculation: a hybrid approach leveraging both a camera and LiDAR technology, merging image data with point cloud information. This method employs AI-driven techniques to identify the individual and their joints, utilizing the cloud-point data for angle computation. The tests, considering different exercises with different perspectives and distances, showed a slight improvement compared to using YOLO v7 for angle calculation. However, the improvement comes with higher system costs when compared with other image-based approaches due to the necessity of equipment such as LiDAR and a loss of fluidity during the exercise performance. Therefore, the cost-benefit of the proposed approach could be questionable. Nonetheless, the results hint at a promising field for further exploration and the potential viability of using the proposed methodology.
- Assessing the Reliability of AI-Based Angle Detection for Shoulder and Elbow RehabilitationPublication . Klein, Luan C.; Chellal, Arezki Abderrahim; Grilo, Vinicius F.S.B.; Gonçalves, José; Pacheco, Maria F.; Fernandes, Florbela P.; Monteiro, Fernando C.; Lima, JoséAngle assessment is crucial in rehabilitation and significantly influences physiotherapists’ decision-making. Although visual inspection is commonly used, it is known to be approximate. This work aims to be a preliminary study about using the AI image-based to assess upper limb joint angles. Two main frameworks were evaluated: MediaPipe and Yolo v7. The study was performed with 28 participants performing four upper limb movements. The results showed that Yolo v7 achieved greater estimation accuracy than Mediapipe, with MAEs of around 5◦ and 17◦, respectively. However, even with better results, Yolo v7 showed some limitations, including the point of detection in only a 2D plane, the higher computational power required to enable detection, and the difficulty of performing movements requiring more than one degree of Freedom (DOF). Nevertheless, this study highlights the detection capabilities of AI approaches, showing be a promising approach for measuring angles in rehabilitation activities, representing a cost-effective and easyto- implement solution.
- Automatic cattle identification using graph matching based on local invariant featuresPublication . Monteiro, Fernando C.Cattle muzzle classification can be considered as a biometric identifier important to animal traceability systems to ensure the integrity of the food chain. This paper presents a muzzle-based classification system that combines local invariant features with graph matching. The proposed approach consists of three phases; namely feature extraction, graph matching, and matching refinement. The experimental results showed that our approach is superior than existing works as ours achieves an all correct identification for the tested images. In addition, the results proved that our proposed method achieved this high accuracy even if the testing images are rotated in various angles.
- Automatic cattle identification using graph matching based on local invariant featuresPublication . Monteiro, Fernando C.Cattle muzzle classification can be considered as a biometric identifier important to animal traceability systems to ensure the integrity of the food chain. This paper presents a muzzle-based classification system that combines local invariant features with graph matching. The proposed approach consists of three phases; namely feature extraction, graph matching, and matching refinement. The experimental results showed that our approach is superior than existing works as ours achieves an all correct identification for the tested images. In addition, the results proved that our proposed method achieved this high accuracy even if the testing images are rotated in various angles.
- Automatic tracking and deformation measurements of red blood cells flowing through a microchannel with a microstenosis: the keyhole modelPublication . Taboada, Bruna Rafaela Pereira; Monteiro, Fernando C.; Lima, Rui A.This study aimed to assess the motion and its deformation index (DI) of red blood cells (RBCs) flowing through a microchannel with a microstenosis using an image analysis-based method. For this purpose, a microchannel having a smooth contraction was used and the images were captured by a standard high-speed microscopy system. An automatic image-processing and analysing method was developed in a MATLAB environment to not only track the motion of RBCs but also measure the DI along the microchannel. The keyhole model, tested in this study, proved to be a promising technique to automatically track individual RBCs in microchannels.
- Beef quality evaluation systemPublication . Teixeira, Cátia; Cadavez, Vasco; Monteiro, Fernando C.Applying computer vision in meat quality evaluation has been an active area of research in recent years. Accurate segmentation of beef-marbling images plays an important role in making the correct decision on beef-marbling score in an automatic beef quality grading system. The purpose of this study was to develop a new segmentation method to correctly separate the fat flecks from the muscle in the rib-eye region in a beef image. The key idea was to measure the percentage of marbling in the muscle to obtain a beef quality evaluation system.
- Beef quality evaluation systemPublication . Teixeira, Cátia; Cadavez, Vasco; Monteiro, Fernando C.The accurate accurate accurate accurate segmentation segmentation segmentation segmentation segmentationof beef -marbling marbling marblingmarbling images images playsplays plays an importantimportant important important role role in making making makingthe correct correct correct correctdecision decision decision decision on beef beefbeef-marbling marbling marbling marblingscore score scorein an automatic automatic automatic beef beef quality quality quality grading grading grading gradingsystemsystem system .The purposepurpose purposepurpose purposeof this thisstudy study studyis to develop develop develop developanew segmentation segmentation segmentation segmentation segmentation method method to correctly correctly correctly correctly separate separate separate separate the fat flecksflecks flecks flecksfrom fromthe musclemuscle muscle musclein the rib -eye region region region of beef images images .
- Beef quality evaluation systemPublication . Teixeira, Cátia; Cadavez, Vasco; Monteiro, Fernando C.The accurate segmentation of beef-marbling images plays an important role in making the correct decision on beef-marbling score in an automatic beef quality grading system. The purpose of this study is to develop a new segmentation method to correctly separate the fat flecks from the muscle in the rib-eye region of beef images.
- Beef quality evaluation systemPublication . Teixeira, Cátia; Cadavez, Vasco; Monteiro, Fernando C.Applying computer vision in meat quality evaluation has been an active area of research in recent years. Accurate segmentation of beef-marbling images plays an important role in making the correct decision on the beef-marbling score in an automatic beef quality grading system. The purpose of this study is to develop a novel segmentation method to correctly separate the fat flecks from the muscle in the rib-eye region in a beef image. This paper presents an automatic system for beef marbling measuring which is composed of discriminant threshold selection method and run length processing. From the experimental results, it has been confirmed that the proposed system enables high quality grading of beef marbling, and robust region segmentation of the actual beef rib-eye image into lean and fat regions.