This is the first book to explore the epistemology and ethics of advanced imaging tests, in order to improve the critical understanding of the nature of knowledge they provide and the practical consequences of their utilization in healthcare. Advanced medical imaging tests, such as PET and MRI, have gained center stage in medical research and in patients’ care. They also increasingly raise questions that pertain to philosophy: What is required to be an expert in reading images? (...) How are standards for interpretation to be fixed? Is there a problem of overutilization of such tests? How should uncertainty be communicated to patients? How to cope with incidental findings? This book is of interest and importance to scholars of philosophy of medicine at all levels, from undergraduates to researchers, to medical researchers and practitioners (radiologists and nuclear physicians) interested in a critical appraisal of the methodology of their discipline and in the ethical principles and consequences of their work. -/- . (shrink)

Medical image segmentation is a key technology for image guidance. Therefore, the advantages and disadvantages of image segmentation play an important role in image-guided surgery. Traditional machine learning methods have achieved certain beneficial effects in medical image segmentation, but they have problems such as low classification accuracy and poor robustness. Deep learning theory has good generalizability and feature extraction ability, which provides a new idea for solving medical image segmentation problems. However, deep learning has problems in terms (...) of its application to medical image segmentation: one is that the deep learning network structure cannot be constructed according to medical image characteristics; the other is that the generalizability y of the deep learning model is weak. To address these issues, this paper first adapts a neural network to medical image features by adding cross-layer connections to a traditional convolutional neural network. In addition, an optimized convolutional neural network model is established. The optimized convolutional neural network model can segment medical images using the features of two scales simultaneously. At the same time, to solve the generalizability problem of the deep learning model, an adaptive distribution function is designed according to the position of the hidden layer, and then the activation probability of each layer of neurons is set. This enhances the generalizability of the dropout model, and an adaptive dropout model is proposed. This model better addresses the problem of the weak generalizability of deep learning models. Based on the above ideas, this paper proposes a medical image segmentation algorithm based on an optimized convolutional neural network with adaptive dropout depth calculation. An ultrasonic tomographic image and lumbar CT medical image were separately segmented by the method of this paper. The experimental results show that not only are the segmentation effects of the proposed method improved compared with those of the traditional machine learning and other deep learning methods but also the method has a high adaptive segmentation ability for various medical images. The research work in this paper provides a new perspective for research on medical image segmentation. (shrink)

In this paper I will argue that medical specialists interpret and diagnose through technological mediations like X-ray and fMRI images, and by actualizing embodied skills tacitly they are determining the identity of objects in the perceptual field. The initial phase of human interpretation of visual objects takes place during the moments of visual perception before we are consciously aware of the perceived. What facilitate this innate ability to interpret are experiences, learning and training that become humanly embodied skills. These (...) embodied skills are actualized during the moments of visual perception. My argument is that biology, society and instruments constitute unique individual ontologies influencing specialist readings of the technological output, in other words, putting limits on the ‘‘truth-to-nature’’ relation, which is so much sought for in science. (shrink)

Topics in Cognitive Science, Volume 14, Issue 2, Page 400-413, April 2022.

Deborah Kirklin discusses the role of medical imaging in the abortion debateThe latest developments in fetal ultrasound technology, made public by a group called Create,1 and first introduced to the wider UK public by the Evening Standard newspaper reporter Isabel Oakeshott in September 2003 and again in July 2004, have evoked a flood of responses from the public, pro-life and pro-choice campaigners, and politicians, re-igniting the debate about abortion in the UK and elsewhere. The focus of the Evening (...) Standard articles, on the smiling, walking, and waving babies that the images purport to show, was echoed throughout the worldwide media coverage that followed. In July 2004, Sir David Steel, sponsor of the 1967 Abortion Act, publicly stated that the Create images led him to believe it was time to review the legal time limit for abortions. Prime Minister Tony Blair said he considered calls for such a review reasonable.What interests me here is the powerful role that biomedical imaging, and the human artifice it involves, can play in influencing the nature, timing, and tone …. (shrink)

Medical imaging has provided insight into the living body that were not possible beforehand. With these methods a revolution in medical diagnosis and biomedical research has begun. Problematic aspects on the other hand are arising from the highly constructive properties of image production, which use complicated physical and physiological effects. Images are established via highly complicated combinations of technology and contingently chosen mathematical and algorithmic solutions. In addition, image construction follows properties of the human visual and cognitive (...) system to allow for the discrimination of the desired categories. It is no wonder that the visualizations referring to the body also show effects which have no physiological correlation within the body. Still such images are often used as if they were one-to-one correlates of the body. This has impacts, e.g. for their use as standardizing instances, resulting in new definitions of the normed healthy body, sickness or pathologies, maleness and femaleness and in determinisms as opposed to the brain's plasticity and variability, both in time and space, inter- as well as intra-individually. (shrink)

A new medical image enhancement algorithm based on spatial frequency domain is presented in this article. The medical image is first divided into several sub-images based on dyadic wavelet scale analysis. At each level, different directional sub-band images can reflect the different characteristics of the image. A low-frequency sub-band image maintains the original image content information, and high-frequency sub-band images represent image details such as edges and regional boundaries. The corresponding sub-band images are then enhanced by different Butterworth (...) homomorphic filtering functions, which can attenuate the low frequencies and amplify the high frequencies. A linear adjustment is carried out on the low frequency of the highest level. Then, the wavelet reconstruction course is used to obtain the final enhanced image. Experiments on magnetic resonance images of temporomandibular joint soft tissues have shown that the proposed method can effectively eliminate the non-uniform luminance distribution of medical images. Its performance is much better than traditional Butterworth homomorphic filtering algorithm whether in subjective vision quality or objective evaluations such as detailed information entropy and average gradient. (shrink)

Contemporary medical imaging technologies produce images on the level of human cells. As a result of such images, egg and sperm cells have become well-known artefacts of popular culture. Medical imaging technology has transformed these gametes from invisible matter integrated in biological processes within the body to identifiable objects. The visualisation of egg and sperm cells has literally lifted the process of human reproduction out of the female body and made the gametes appear as protagonists in (...) the story of human reproduction. The article argues that visualisation of the gametes and the central role they play in contemporary imaginations of reproduction may offer vital contributions to the rather rapid acceptance and normalisation of assisted reproduction. (shrink)

A reliable medical image management must provide proper security for patient information. Protecting the medical information of the patients is a major concern in all hospitals. Digital watermarking is a procedure prevalently used to secure the confidentiality of medical information and maintain them, which upgrades patient health awareness. To protect the medical information, the robust and lossless patient medical information sharing system using crypto-watermarking method is proposed. The proposed system consists of two phases: embedding and (...) extraction. In this paper, we securely share three types of patient information, medical image, electronic health record, and face image from one hospital to another hospital. Initially, all the three inputs are encrypted and the information is concordant. In order to enhance the robustness of the crypto-watermarking system, the obtained bit stream is compressed, and the compressed bit streams are embedded into the cover image. The same process is repeated for the extraction process. The experimentation result is carried out using different medical images with EHR, and the effectiveness of the proposed algorithm is analyzed with the help of peak signal to noise ratio. (shrink)

Human disease identification from the scanned body parts helps medical practitioners make the right decision in lesser time. Image segmentation plays a vital role in automated diagnosis for the delineation of anatomical organs and anomalies. There are many variants of segmentation algorithms used by current researchers, whereas there is no universal algorithm for all medical images. This paper classifies some of the widely used medical image segmentation algorithms based on their evolution, and the features of each generation (...) are also discussed. The comparative analysis of segmentation algorithms is done based on characteristics like spatial consideration, region continuity, computation complexity, selection of parameters, noise immunity, accuracy, and computation time. Finally, in this work, some of the typical segmentation algorithms are implemented on real-time datasets using Matlab 2010 software, and the outcome of this work will be an aid for the researchers in medical image processing. (shrink)

Blood is a vital body fluid and can be instrumental in identifying various pathological conditions. Nowadays, a lot of people are suffering from COVID-19 and every country has its own limited testing capacity. Consequently, a system is required to help doctors analyze a patient’s blood structure including COVID-19. Therefore, in this paper, we extracted and selected blood features by proposing a new feature extraction and selection method named stepwise linear discriminant analysis. SWLDA emphasizes on picking confined features from blood structure (...) images and discerning its class based on reversion value such as partial F value. SWLDA begins with picking an equivalence comprising the sole finest X variable and then puts in effort to add more Xs individually, providing the situations are adequate. The process of adding and picking is based on F value to determine which variable would be entered. Then, the picked or the default F-to-enter value is compared with the uppermost partial F value. After this step, the forward addition or backward removal begins and whether the partial test values for all the predictor variables already in the line are estimated is known. Then, the comparison is made between the lowermost partial test value and preselected or defaulting consequence levels such as F0. Finally, the system is trained by employing support vector machine to label the blood images. The performance of the proposed approach is assessed by employing 8 different datasets of blood structures. It is assured that the proposed method has achieved significant results under different blood structure images including COVID-19. (shrink)

The challenge for those treating or witnessing pain is to find a way of crossing the chasm of meaning between them and the person living with pain. This paper proposes that images can strengthen agency in the person with pain, particularly but not only in the clinical setting, and can create a shared space within which to negotiate meaning. It draws on multidisciplinary analyses of unique material resulting from two fine art/medical collaborations in London, UK, in which the invisible (...) experience of pain was made visible in the form of co-created photographic images, which were then made available to other patients as a resource to use in specialist consultations. In parallel with the pain encounters it describes, the paper weaves together the insights of specialists from a range of disciplines whose methodologies and priorities sometimes conflict and sometimes intersect to make sense of each other’s findings. A short section of video footage where images were used in a pain consultation is examined in fine detail from the perspective of each discipline. The analysis shows how the images function as ‘transactional objects’ and how their use coincides with an increase in the amount of talk and emotional disclosure on the part of the patient and greater non-verbal affiliative behaviour on the part of the doctor. These findings are interpreted from the different disciplinary perspectives, to build a complex picture of the multifaceted, contradictory and paradoxical nature of pain experience, the drive to communicate it and the potential role of visual images in clinical settings. (shrink)

A discussion of Christian ethics focuses on the physician's image as a parent, warrior against death, expert, and teacher, and the oath that guides his or her practice.

The consent process for publication of clinical images in medical journals varies widely. The extent of this variation is not known. It is also not known whether journals follow their own stated best practices or the guidance of the International Committee of Medical Journal Editors. We assessed consent requirements in a sample of 10 top impact factor general medicine journals that publish clinical images, examining variability in consent requirements for clinical image publication and congruence of requirements with the (...) recommendations of the ICMJE. Clinical image consent requirements varied widely from journal to journal. None of the studied journals, even amongst n = 4 ICMJE members or n = 8 journals who self-report adherence to ICMJE guidelines, comply with all of the recommendations of the ICMJE. Half of studied journals require a journal-specific consent form. Among top medical journals there is significant heterogeneity in consent requirements for clinical images. Variability of consent requirements is neither practical nor rational; inconsistent requirements create uncertainty for authors, present impediments to dissemination of scholarship, and undermine a shared professional understanding of how best to protect patient privacy. We propose adopting a standardized consent form and process for publication of identifiable images in medical journals, with uniform elements and explicit definitions. (shrink)

Advanced medical imaging, such as CT, fMRI and PET, has undergone enormous progress in recent years, both in accuracy and utilization. Such techniques often bring with them an illusion of immediacy, the idea that the body and its diseases can be directly inspected. In this paper we target this illusion and address the issue of the reliability of advanced imaging tests as knowledge procedures, taking positron emission tomography in oncology as paradigmatic case study. After individuating a suitable (...) notion of reliability, we argue that PET is a highly theory-laden and non-immediate knowledge procedure, in spite of the photographic-like quality of the images it delivers; the diagnostic conclusions based on the interpretation of PET images are population-dependent; PET images require interpretation, which is inherently observer-dependent and therefore variable. We conclude with a three-step methodological proposal for enhancing the reliability of advanced medical imaging. (shrink)

In concept, an image has both verticality and horizontal dimensions. Saturated images within this space have a horizon and can exceed that horizon. Within that horizon where the image dwells something chances itself upon the observer and the observed. Into that public space between self and other, students bring an instrumental approach to how they plan to deploy their new fund of knowledge, only to discover that the setting itself has become an event where surprise and upheaval disrupt their illusion (...) of self-continuity and the façade of familiarity. Phenomenologically, upheaval shows itself when givenness both precedes and participates in the giving of phenomena such as medical students’ “before and after” images of psychoanalysis. They discover and reconfigure their erstwhile absolute positions and values into reconfigurations of self and prior commitments. The turning point from their instrumental use of knowledge to reconfigurations of how they situate themselves in the world decisively comes when teaching and learning become an event in se that disturbs their sense of order.Following Husserl, phenomenological psychological observation has required us to go from the events of history to a sense of history. Would, however, that we could stay at the level of events much longer to see images explode and exceed their horizons from the illusion of order, and patterned repetition disrupted by surprise, upheaval and indeterminacy in the spirit of Alain Badiou! (shrink)

Russo and Williamson (Int Stud Philos Sci 21(2):157–170, 2007) put forward the thesis that, at least in the health sciences, to establish the claim that C is a cause of E, one normally needs evidence of an underlying mechanism linking C and E as well as evidence that C makes a difference to E. This epistemological thesis poses a problem for most current analyses of causality which, in virtue of analysing causality in terms of just one of mechanisms or difference (...) making, cannot account for the need for the other kind of evidence. Weber (Int Stud Philos Sci 23(2):277–295, 2009) has suggested to the contrary that Giere’s probabilistic analysis of causality survives this criticism. In this paper, we look in detail at the case of medical imaging technology, which, we argue, supports the thesis of Russo and Williamson, and we respond to Weber’s suggestion, arguing that Giere’s account does not survive the criticism. (shrink)

Radiography images are widely utilized in the health sector to recognize the patient health condition. The noise and irrelevant region information minimize the entire disease detection accuracy and computation complexity. Therefore, in this study, statistical Kolmogorov–Smirnov test has been integrated with wavelet transform to overcome the de-noising issues. Then the cat swarm-optimized deep belief network is applied to extract the features from the affected region. The optimized deep learning model reduces the feature training cost and time and improves the overall (...) disease detection accuracy. The network learning process is enhanced according to the AdaDelta learning process, which replaces the learning parameter with a delta value. This process minimizes the error rate while recognizing the disease. The efficiency of the system evaluated using image retrieval in medical application dataset. This process helps to determine the various diseases such as breast, lung, and pediatric studies. (shrink)

Given that visualisations via medical imaging have tremendously increased over the last decades, the overall presence of colour-coded brain slices generated on the basis of functional imaging, i.e. neuroimaging techniques, have led to the assumption of so-called kinds of brains or cognitive profiles that might be especially related to non-healthy humans affected by neurological, neuropsychological or psychiatric syndromes or disorders. In clinical contexts especially, one must consider that visualisations through medical imaging are suggestive in a (...) twofold way. Imaging data not only visually render pathological entities, but also tend to represent objective and concrete evidence for these psychophysical states in question. This article aims to identify key issues in visually rendering psychiatric disorders via functional approaches of imaging within the neurosciences from an epistemological point of view. (shrink)

The main idea behind this work is to present three-dimensional image visualization through two-dimensional images that comprise various images. 3D image visualization is one of the essential methods for excerpting data from given pieces. The main goal of this work is to figure out the outlines of the given 3D geometric primitives in each part, and then integrate these outlines or frames to reconstruct 3D geometric primitives. The proposed technique is very useful and can be applied to many kinds of (...) images. The experimental results showed a very good determination of the reconstructing process of 2D images. (shrink)

Medicine and astronomy were both scientific disciplines to which visual demonstration proved helpful, were taught in the universities, and were deeply influenced by humanism and by the development of print culture, but they did not use printed images in the same way. Thus, all the aspects of astronomical activity benefited from the accompaniment of printed images, whereas, even for anatomy, illustration does not seem to have been seen as a necessity in Renaissance medical books. To explore such a difference, (...) the chronology of the development of illustration in both fields (from the first illustrated incunabula to the mid-sixteenth century) is compared, and some explanations (economical, epistemological, cultural) are proposed and questioned. -/- . (shrink)

This article analyzes how the medical gaze made possible by MRI operates in radiological laboratories. It argues that although computer-assisted medical imaging technologies such as MRI shift radiological analysis to the realm of cyborg visuality, radiological analysis continues to depend on visualization produced by other technologies and diagnostic inputs. In the radiological laboratory, MRI is used to produce diverse sets of images of the internal parts of the body to zero in and visually extract the pathology. Visual (...) extraction of pathology becomes possible, however, because of the visual training of the radiologists in understanding and interpreting anatomic details of the whole body. These two levels of viewing constitute the bifocal vision of the radiologists. To make these levels of viewing work complementarily, the body, as it is presented in the body atlases, is made notational. (shrink)

At present, most of the research in the field of medical-assisted diagnosis is carried out based on image or electronic medical records. Although there is some research foundation, they lack the comprehensive consideration of comprehensive image and text modes. Based on this situation, this article proposes a fusion classification auxiliary diagnosis model based on GoogleNet model and Bi-LSTM model, uses GoogleNet to process brain computed tomographic images of ischemic stroke patients and extract CT image features, uses Bi-LSTM model (...) to extract the electronic medical record text, integrates the two features using the full connection layer network and Softmax classifier, and obtains a method that can assist the diagnosis from two modes. Experiments show that the proposed scheme on average improves 3.05% in accuracy compared to individual image or text modes, and the best performing GoogleNet + Bi-LSTM model achieves 96.61% accuracy; although slightly less in recall, it performs better on F1 values, and has provided feasible new ideas and new methods for research in the field of multi-model medical-assisted diagnosis. (shrink)

To assess the effectiveness of Visual Thinking Strategies in medical education curricula, a pretest–posttest experimental study design was used to evaluate the impact of participating in VTS workshops on first-year medical students. A total of forty-one intervention and sixty comparative students completed the study which included the analysis of clinical images followed by a measurement of word count, length of time analyzing images, and quality of written observations of clinical images. VTS training increased the total number of words (...) used to describe clinical images, the time spent analyzing the images, and the number of clinically relevant observations. (shrink)

This article presents a case study of a recent controversy over the use of computed tomography as a diagnostic technology in South Korean hospitals. The controversy occurred in the wake of a series of conflicts in the late twentieth century over the legitimate placement of healing practices, medicinal substances, and medical technologies within Korea’s separate “Western Medicine” and “Korean Medicine” systems of health care and pharmaceutical distribution. The controversy concerned an attempt to use hi-tech imaging technology—the epitome of (...) modern medicine—in a clinic that maintains a strong ideological attachment to Korean healing traditions. A close study of this dispute, based on interviews, participant observation, and documentary analysis, showed that discursive positions taken about the translatability of medical technologies changed with the context of dispute, and did not reflect a stable epistemic boundary between rival medical paradigms. (shrink)

A Bayesian approach using wavelet coefficient modeling is proposed for de-noising additive white Gaussian noise in medical magnetic resonance imaging. In a parallel acquisition process, the magnetic resonance image is affected by white Gaussian noise, which is additive in nature. A normal inverse Gaussian probability distribution function is taken for modeling the wavelet coefficients. A Bayesian approach is implemented for filtering the noisy wavelet coefficients. The maximum likelihood estimator and median absolute deviation estimator are used to find the (...) signal parameters, signal variances, and noise variances of the distribution. The minimum mean square error estimator is used for estimating the true wavelet coefficients. The proposed method is simulated on MRI. Performance and image quality parameters show that the proposed method has the capability to reduce the noise more effectively than other state-of-the-art methods. The proposed method provides 8.83%, 2.02%, 6.61%, and 30.74% improvement in peak signal-to-noise ratio, structure similarity index, Pratt’s figure of merit, and Bhattacharyya coefficient, respectively, over existing well-accepted methods. The effectiveness of the proposed method is evaluated by using the mean squared difference parameter. MSD shows the degree of dissimilarity and is 0.000324 for the proposed method, which is less than that of the other existing methods and proves the effectiveness of the proposed method. Experimental results show that the proposed method is capable of achieving better signal-to-noise ratio performance than other tested de-noising methods. (shrink)

This textbook brings the humanities to students in order to evoke the humanity of students. It helps to form individuals who take charge of their own minds, who are free from narrow and unreflective forms of thought, and who act compassionately in their public and professional worlds. Using concepts and methods of the humanities, the book addresses undergraduate and premed students, medical students, and students in other health professions, as well as physicians and other healthcare practitioners. It encourages them (...) to consider the ethical and existential issues related to the experience of disease, care of the dying, health policy, religion and health, and medical technology. Case studies, images, questions for discussion, and role-playing exercises help readers to engage in the practical, interpretive, and analytical aspects of the material, developing skills for critical thinking as well as compassionate care. (shrink)