Publications by authors named "Jan Juszczyk"

Artifacts in computed tomography and X-ray microtomography are image distortions caused by various factors. Some can be reduced before or during the examination, while others are removed algorithmically after image acquisition. The latter group includes metallic artifacts caused by metal objects in the sample.

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This study addresses computer-aided breast cancer diagnosis through a hybrid framework for breast tumor segmentation in ultrasound images. The core of the three-stage method is based on the autoencoder convolutional neural network. In the first stage, we prepare a hybrid pseudo-color image through multiple instances of fuzzy connectedness analysis with a novel distance-adapted fuzzy affinity.

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Nowadays, a very important motivation for the development of new functional materials for medical purposes is not only their performance but also whether they are environmentally friendly. In recent years, there has been a growing interest in the possibility of labelling (bio)degradable polymers, in particular those intended for specific applications, especially in the medical sector, and the potential of information storage in such polymers, making it possible, for example, to track the ultimate environmental fate of plastics. This article presents a straightforward green approach that combines both aspects using an oligopeptide, which is an integral part of polymer material, to store binary information in a physical mixture of polymer and oligopeptide.

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Over the last few decades, high-frequency ultrasound has found multiple applications in various diagnostic fields. The fast development of this imaging technique opens up new diagnostic paths in dermatology, allergology, cosmetology, and aesthetic medicine. In this paper, being the first in this area, we discuss the usability of HFUS in anti-aging skin therapy assessment.

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This study aims at high-frequency ultrasound image quality assessment for computer-aided diagnosis of skin. In recent decades, high-frequency ultrasound imaging opened up new opportunities in dermatology, utilizing the most recent deep learning-based algorithms for automated image analysis. An individual dermatological examination contains either a single image, a couple of pictures, or an image series acquired during the probe movement.

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In this study, we investigate perspectives for thermal tomography based on planar infrared thermal images. Volumetric reconstruction of temperature distribution inside an object is hardly applicable in a way similar to ionizing-radiation-based modalities due to its non-penetrating character. Here, we aim at employing the autoencoder deep neural network to collect knowledge on the single-source heat transfer model.

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A multimodal wound image database was created to allow fast development of computer-aided approaches for wound healing monitoring. The developed system with parallel camera optical axes enables multimodal images: photo, thermal, stereo, and depth map of the wound area to be acquired. As a result of using this system a multimodal database of chronic wound images is introduced.

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An automated vendor-independent system for dose monitoring in computed tomography (CT) medical examinations involving ionizing radiation is presented in this paper. The system provides precise size-specific dose estimates (SSDE) following the American Association of Physicists in Medicine regulations. Our dose management can operate on incomplete DICOM header metadata by retrieving necessary information from the dose report image by using optical character recognition.

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Percutaneous ablation methods are used to treat primary and metastatic liver tumors. Image guided navigation support minimally invasive interventions of rigid anatomical structures. When working with the displacement and deformation of soft tissues during surgery, as in the abdomen, imaging navigation systems are in the preliminary implementation stage.

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Fast development of imaging techniques in last decades has offered the intra-operative visualization as the integral part of surgical tools. Therefore, on-going research activities still focus on efficient and robust analysis of ultrasound images. The paper meets these requirements targeting in detection of biopsy needle, estimation of the needle trajectory and tracking the needle tip motion inside the examined tissue.

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Purpose: A growing number of medical applications, including minimal invasive surgery, depends on multi-modal or multi-sensors data processing. Fast and accurate 3D scene analysis, comprising data registration, seems to be crucial for the development of computer aided diagnosis and therapy. The advancement of surface tracking system based on optical trackers already plays an important role in surgical procedures planning.

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The paper presents the multistep methodology of bimodal Patient Specific Phantom (PSP) development. First, CT based abdominal digital model is designed. It serves as a source for designing organ moulds manufactured by means of a 3D-printer.

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Detection of region specific voxel is a true challenge in many segmentation procedures. In this study a concept of implementing granular computing in the detection of anatomical structures in abdominal computed tomography (CT) scans is introduced. After proving the usefulness of the information granules to identify voxels that mark certain organs, an automatic model-based approach has been developed.

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There is an agreement about the principle that bones are optimized to resist daily loads. This has never been ascertained for the human tibia. One of the main load components in the tibia in vivo is a cantilever load (with a linearly varying bending moment, with its largest component in the sagittal plane).

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Background: Ewing sarcoma is a malignant, small round cell bone tumor, presenting predominantly in children and adolescents. Ewing sarcoma may develop in every bone; diaphyses of long bones, ribs and flat bones are the main locations. Local and systemic clinical symptoms are nonspecific - pain, swelling, fever or ill-being.

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