Aujourd’hui, des appareils capables de capter et de traiter les images peuvent être trouvés dans les systèmes complexes de surveillance ou de simples téléphones mobiles. Dans certaines applications, le temps nécessaire au traitement des images n’est pas aussi important que la qualité du traitement (par exemple, l’imagerie médicale). Par contre, dans d’autres cas, la qualité peut être sacrifiée au profit du facteur temps. Cette thèse se concentre sur ce dernier cas, et propose deux types de méthodes rapides pour l’amélioration du contraste d’image. Les méthodes proposées sont fondées sur l’égalisation d’histogramme (EH), et certaines s’adressent à des images en niveaux de gris, tandis que d’autres s’adressent à des images en couleur. En ce qui concerne les méthodes EH pour des images en niveaux de gris, les méthodes actuelles tendent à changer la luminosité moyenne de l’image de départ pour le niveau moyen de l´interval de niveaux de gris. Ce n’est pas souhaitable dans le cas de l’amélioration du contraste d’image pour les produits de l’électronique grand-public, où la préservation de la luminosité de l’image de départ est nécessaire pour éviter la production de distortions dans l’image de sortie. Pour éviter cet inconvénient, des méthodes de Biégalisation d’histogrammes pour préserver la luminosité et l’amélioration du contraste ont été proposées. Bien que ces méthodes préservent la luminosité de l’image de départ tout en améliorant fortement le contraste, elles peuvent produire des images qui ne donnent pas une impression visuelle aussi naturelle que les images de départ. Afin de corriger ce problème, nous proposons une technique appelée multi-EH, qui consiste à décomposer l’image en plusieurs sous-images, et à appliquer le procédé classique de EH à chacune d’entre elles. Bien que produisant une amélioration du contraste moins marquée, cette méthode produit une image de sortie d’une apparence plus naturelle. Nous proposons deux fonctions de décalage par découpage d’histogramme, permettant ainisi de concevoir deux nouvelle méthodes de multi-EH. Une fonction de coût est également utilisé pour déterminer automatiquement en combien de sous-images l’histogramme de l’image d’entrée sera décomposée. Les expériences montrent que nos méthodes sont meilleures pour la préservation de la luminosité et produisent des images plus naturelles que d´autres méthodes de EH. Pour améliorer le contraste dans les images en couleur, nous introduisons une méthode 5 Résumé 6 générique et rapide, qui préserve la teinte. L’égalisation d’histogramme est fondée sur l’espace couleur RGB, et nous proposons deux instantiations de la méthode générique. La première instantiation utilise des histogrammes 1D R-red, G-green, et B-bleu afin d’estimer l’histogramme 3D RGB qui doit être égalisé, alors que le deuxième instantiation utilise des histogrammes 2D RG, RB, et GB. L’égalisation d’histogramme est effectué en utilisant des transformations de décalage qui préservent la teinte, en évitant l’apparition de couleurs irréalistes. Nos méthodes ont des complexités de temps et d’espace linéaire, par rapport à la taille de l’image, et n’ont pas besoin de faire la conversion d’un espace couleur à l’autre afin de réaliser l’amélioration du contraste de l’image. Des évaluations objectives comparant nos méthodes et d’autres ont été effectuées au moyen d’une mesure de contraste et de couleur afin de mesurer la qualité de l’image, où la qualité est établie comme une fonction pondérée d’un indice de “naturalité” et d’un indice de couleur. Nous analysons 300 images extraites d’une base de données de l’Université de Berkeley. Les expériences ont montré que la valeur de contraste de l’image produite par nos méthodes est en moyenne de 50% supérieure à la valeur de contraste de l’image original, tout en conservant une qualité des images produites proche de celle des images originales / Nowadays devices are able to capture and process images from complex surveillance monitoring systems or from simple mobile phones. In certain applications, the time necessary to process the image is not as important as the quality of the processed images (e.g., medical imaging), but in other cases the quality can be sacrificed in favour of time. This thesis focuses on the latter case, and proposes two methodologies for fast image contrast enhancement methods. The proposed methods are based on histogram equalization (HE), and some for handling gray-level images and others for handling color images As far as HE methods for gray-level images are concerned, current methods tend to change the mean brightness of the image to the middle level of the gray-level range. This is not desirable in the case of image contrast enhancement for consumer electronics products, where preserving the input brightness of the image is required to avoid the generation of non-existing artifacts in the output image. To overcome this drawback, Bi-histogram equalization methods for both preserving the brightness and contrast enhancement have been proposed. Although these methods preserve the input brightness on the output image with a significant contrast enhancement, they may produce images which do not look as natural as the ones which have been input. In order to overcome this drawback, we propose a technique called Multi-HE, which consists of decomposing the input image into several sub-images, and then applying the classical HE process to each one of them. This methodology performs a less intensive image contrast enhancement, in a way that the output image presented looks more natural. We propose two discrepancy functions for image decomposition which lead to two new Multi-HE methods. A cost function is also used for automatically deciding in how many sub-images the input image will be decomposed on. Experimental results show that our methods are better in preserving the brightness and producing more natural looking images than the other HE methods. In order to deal with contrast enhancement in color images, we introduce a generic fast hue-preserving histogram equalization method based on the RGB color space, and two instances of the proposed generic method. The first instance uses R-red, G-green, and Bblue 1D histograms to estimate a RGB 3D histogram to be equalized, whereas the second instance uses RG, RB, and GB 2D histograms. Histogram equalization is performed using 7 Abstract 8 shift hue-preserving transformations, avoiding the appearance of unrealistic colors. Our methods have linear time and space complexities with respect to the image dimension, and do not require conversions between color spaces in order to perform image contrast enhancement. Objective assessments comparing our methods and others are performed using a contrast measure and color image quality measures, where the quality is established as a weighed function of the naturalness and colorfulness indexes. This is the first work to evaluate histogram equalization methods with a well-known database of 300 images (one dataset from the University of Berkeley) by using measures such as naturalness and colorfulness. Experimental results show that the value of the image contrast produced by our methods is in average 50% greater than the original image value, and still keeping the quality of the output images close to the original / Dispositivos para aquisição e processamento de imagens podem ser encontrados em sistemas complexos de monitoração de segurança ou simples aparelhos celulares. Em certas aplicações, o tempo necessário para processar uma imagem não é tão importante quanto a qualidade das imagens processadas (por exemplo, em imagens médicas), mas em alguns casos a qualidade da imagem pode ser sacrificada em favor do tempo. Essa tese se foca nesse último caso, e propõe duas metodologias eficientes para o realce de contraste de imagens. Os métodos propostos são baseados em equalização de histograma (EH), e focam em imagens em tons de cinza e em imagens coloridas. Os métodos baseados em EH atualmente utilizados para processar imagens em tons de cinza tendem a mudar o brilho médio da imagem para o tom médio do intervalo de tons de cinza. Essa mudança não é desejavél em aplicações que visam melhorar o contraste em produtos eletrônicos utilizados pelo consumidor, onde preservar o brilho da imagem original é necessário para evitar o aparecimento de artefatos não exitentes na imagem de saída. Para corrigir esse problema, métodos de bi-equalização de histogramas para preservação do brilho e contraste de imagens foram propostos. Embora esses métodos preservem o brilho da imagem original na imagem processada com um realce significante do contraste, eles podem produzir imagens que não parecem naturais. Esse novo problema foi resolvido por uma nova técnica chamada de Multi-Equalização de histogramas, que decompõe a imagem original em várias sub-imagens, e aplica o método de EH clássico em cada uma delas. Essa metodologia realiza um realce de contraste menos intenso, de forma que a imagem processada parece mais “natural”. Essa tese propõe duas novas funções de discrepância para decomposição de imagens, originando dois novos métodos de Multi-EH. Além disso, uma função de custo é utilizada para determinar em quantas sub-imagens a imagem original será dividida. Através da comparação objetiva e quantitative usando uma medida de constrate, os experimentos mostraram que os métodos propostos são melhores que outros EH estudados, uma vez que eles preservam o brilho e produzem imagens com uma aparência mais natural. Em relação aos métodos para realce de contraste em imagens coloridas, essa tese propõe um método genérico e eficiente de EH baseado no espaço de cores RGB que preserva o tom 9 Resumo 10 (a matiz), e implementa duas instâncias desse método genérico. A primeira instância utiliza os histogramas 1D R-red, G-green e B-blue para estimar um histograma 3D RGB, que é então equalizado. A segunda instância, por sua vez, utiliza os histogramas 2D RG, RB, e GB. A EH é executada utilizando transformadas de deslocamento que preservam a tonalidade da cor, evitando o aparecimento de cores não reais. Os métodos propostos tem complexidade linear no espaço e no tempo em relação ao tamanho da imagem, e não usam nenhuma conversão de um espaço de cores para outro. As imagens produzidas foram avaliadas objetivamente, comparando os métodos propostos com outros estudados. A avaliação objetiva foi feita utilizando medidas de contraste e de qualidade da cor da imagem, onde a qualidade foi definida como uma função ponderada dos índices de naturalidade e cromicidade. Um conjunto de 300 imagens extraídas da base de dados da Universidade de Berkeley foram analisadas. Os experimentos mostraram que o valor do contraste das imagens produzidas pelos métodos propostos é, em médias, 50% maior que o valor do contraste na imagem original, e ao mesmo tempo a qualidade das imagens produzidas é próxima a qualidade da imagem original / Dispositivi per l’acquisizione e lo svolgimento di immagini si possono trovare nei complessi sistemi di monitoramento di sicurezza o nei semplici cellulari. In alcune applicazioni il tempo necessario per svolgere un’immagine non è cosi importante come la qualità delle immagini svolte (es. nelle immagini mediche), ma in alcuni casi la qualità dell’immagine potrà venire daneggiata a favore del tempo. Questa tesi è basata su quest’ultimo caso e propone due metodi efficienti per evidenziare il contrasto di colore delle immagini. I metodi proposti vengono basate sull’equalizazzione d’istogramma (EI), mirati su delle immagini grigie e sulle immagini colorate. I metodi basati sull’EI attualmente utilizzati per svolgere delle immagini grigie tendono a cambiare il brillo medio dell’immagine per il tono medio dell’intervallo grigio. Questo cambiamento non è desiderato nelle applicazioni mirate al miglioramento del contrasto sui prodotti elettronici utilizzati dal consumatore, dove preservare il brillo dell’immagine originale è necessario per evitare la comparsa di artefatti inesistenti nell’immagine d’uscita. Sono stati proposti dei metodi di biequalizazzione di istogrammi per corregere questo problema della preservazione del brillo e del contrasto di colore delle immagini. Nonostante questi metodi preservino il brillo dell’immagine originale con significante rilievo del contrasto nell’immagine svolta, questi possono produrre delle immagini che non sembrino naturali. Questo nuovo problema è stato risolto con una nuova tecnica detta Multiequalizazzione di istogrammi, che decompone l’immagine originale in varie sottoimmagini, applicando su ognuna di queste il metodo EI classico. Questa metodologia realizza un contrasto di rilievo meno intenso in modo che l’immagine svolta sembri più “naturale”. Questa tesi propone due nuove funzioni di discrepanza per la decomposizione delle immagini, originandone due nuovi metodi Multi-EI. Inoltre una funzione di costo viene utilizzata per determinare in quante sottoimmagini l’immagine originale verrà divisa. Attraverso paragone obiettivo e quantitativo, usando una misura di contrasto, gli esperimenti hanno convalidato che i metodi proposti sono migliori di quegli EI studiati perché quelli preservano il brillo e producono immagini con un’apparenza più naturale. Con riferimento ai metodi utilizzati per rilevare il contrasto nelle immagini colorate questa tese propone un metodo generico ed efficiente di EI, in base negli spazi di colori 11 Risumo 12 RGB, che preserva il tono (la sfumatura) e implementa due istanze di questo metodo generico. La prima istanza utilizza gli istogrammi 1D R-Red, G-green e B-blue per stimare un istogramma 3D RGB, che viene di seguito equalizzato. La seconda istanza invece utilizza gli istogrammi 2D RG, RB e GB. La EI viene eseguita utilizzando trasformate di trasloco che preservano il tono del colore, evitando così la comparsa di colori non reali. I metodi proposti hanno complessità lineare nello spazio e nel tempo rispetto alla grandezza dell’immagine e non usano nessuna conversione da un spazio di colore all’altro. Le immagini prodotte sono state valutate in modo obiettivo, paragonando i metodi proposti con gli altri studiati. La valutazione obiettiva è stata fatta utilizzando delle misure di contrasto e qualità del colore dell’immagine, dove la qualità è stata definita come una funzione ponderata degli indici di naturalità e colorito. Si analisarano un insieme di 300 immagini tratte dalla base dei dati dell’Università di Berkeley. Gli sperimenti mostrarono che il valore del contrasto delle immagini prodotte daí metodi proposti è mediamente 50% maggiore del valore del contrasto nell’immagine originale e una volta ancora la qualità delle immagini prodotte è vicina alla qualità dell’immagine originale

Contrast is an important factor in any subjective evaluation of image quality. It is the difference in visual properties that makes an object distinguishable from other objects and background. Contrast Enhancement method is mainly used to enhance the contrast in the image by using its Histogram. Histogram is a distribution of numerical data in an image using graphical representation. Histogram Equalization is widely used in image processing to adjust the contrast in the image using histograms. Whereas Gamma Correction is often used to adjust luminance in an image. By combining Histogram Equalization and Gamma Correction we proposed a hybrid method, that is used to modify the histograms and enhance contrast of an image in a digital method. Our proposed method deals with the variants of histogram equalization and transformed based gamma correction. Our method is an automatically transformation technique that improves the contrast of dimmed images via the gamma correction and probability distribution of luminance pixels. The proposed method is converted into an android application. We succeeded in enhancing the contrast of an image by using our method and we have tested for different alpha values. Graphs of the gamma for different alpha values are plotted.

京都大学 / 0048 / 新制・課程博士 / 博士(医学) / 甲第22743号 / 医博第4661号 / 新制||医||1046(附属図書館) / 京都大学大学院医学研究科医学専攻 / (主査)教授 花川 隆, 教授 渡邉 大, 教授 黒田 知宏 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

L'amélioration d'image est une sorte de technique pour améliorer la qualité visuelle d'image, qui joue un rôle très important dans les domaines du traitement d'image et de la vision d'ordinateur. En particulier, nous considérons la correction de couleur et l'amélioration de contraste pour améliorer la qualité d'image.Dans la première partie de cette thèse, nous nous concentrons sur la correction des couleurs pour les images naturelles. Tout d'abord, nous donnons un examen simple de la correction des couleurs. Deuxièmement, nous proposons une méthode efficace de correction des couleurs pour la couture d'images via la spécification d'histogramme et la cartographie globale. Troisièmement, nous présentons une approche de cohérence des couleurs pour les collections d'images, basée sur la spécification de la gamme conservation histogramme.Dans la deuxième partie, nous prêtons attention à l'amélioration du contraste pour les images et les vidéos naturelles. Tout d'abord, nous donnons un simple examen de l'amélioration du contraste. Deuxièmement, nous proposons une méthode de préservation du contraste global de naturalité, qui peut éviter une survalorisation. Troisièmement, nous présentons une méthode de fusion à base de variation pour l'amélioration de l'image d'illumination non uniforme, qui peut éviter la sur-amplification ou la sous-amélioration. Enfin, nous étendons le cadre basé sur la fusion pour améliorer les vidéos avec une stratégie temporellement cohérente, qui n'entraîne pas de scintillement des artefacts. / Image enhancement is a kind of technique to improve the image visual quality, which plays a very important role in the domains of image processing and computer vision. Specifically, we consider color correction and contrast enhancement to improve the image quality.In the first part of this thesis, we focus on color correction for natural images. Firstly, we give a simple review of color correction. Secondly, we propose an efficient color correction method for image stitching via histogram specification and global mapping. Thirdly, we present a color consistency approach for image collections, based on range preserving histogram specification.In the second part, we pay attention to contrast enhancement for natural images and videos. Firstly, we give a simple review of contrast enhancement. Secondly, we propose a naturalness preservation global contrast enhancement method, which can avoid over-enhancement. Thirdly, we present a variational-based fusion method for non-uniform illumination image enhancement, which can avoid overenhancement or under-enhancement. Finally, we extend the fusion-based framework to enhance videos with a temporally consistent strategy, which does not result in flickering artifacts.

<p>This dissertation addresses the challenges of giving computers the ability of doing face recognition, i.e. discriminate between different faces. Face recognition systems are commonly trained with a database of face images, becoming “familiar” with the given faces. Many reported methods rely heavily on training database size and representativenes. But collecting training images covering, for instance, a wide range of viewpoints, different expressions and illumination conditions is difficult and costly. Moreover, there may be only one face image per person at low image resolution or quality. In these situations, face recognition techniques usually suffer serious performance drop. Here we present effective algorithms that deal with single image per person database, despite issues with illumination, face expression and pose variation.</p><p>Illumination changes the appearance of a face in images. Thus, we use a new pyramid based fusion method for face recognition under arbitrary unknown lighting. This extended approach with logarithmic transform works efficiently with a single image. The produced image has better contrast at both low and high ranges, i.e. has more visible details than the original one. An improved method works with high dynamic range images, useful for outdoor face images.</p><p>Face expressions also modify the images’ appearance. An extended Hidden Markov Models (HMM) with a flexible encoding scheme treats images as an ensemble of horizontal and vertical strips. Each person is modeled by Joint Multiple Hidden Markov Models (JM-HMMs). This approach offers computational advantages and the good learning ability from just a single sample per class. A fast method simulated JM-HMM functionality is then derived. The new method with abstract observations and a simplified similarity measurement does not require retraining HMMs for new images or subjects. Pose invariant recognition from a single sample image per person was overcome by using the wire frame Candide face model for the synthesis of virtual views. This is one of the support functions of our face recognition system, WAWO. The extensive experiments clearly show that WAWO outperforms the state-of-the-art systems in FERET tests.</p>

This dissertation addresses the challenges of giving computers the ability of doing face recognition, i.e. discriminate between different faces. Face recognition systems are commonly trained with a database of face images, becoming “familiar” with the given faces. Many reported methods rely heavily on training database size and representativenes. But collecting training images covering, for instance, a wide range of viewpoints, different expressions and illumination conditions is difficult and costly. Moreover, there may be only one face image per person at low image resolution or quality. In these situations, face recognition techniques usually suffer serious performance drop. Here we present effective algorithms that deal with single image per person database, despite issues with illumination, face expression and pose variation. Illumination changes the appearance of a face in images. Thus, we use a new pyramid based fusion method for face recognition under arbitrary unknown lighting. This extended approach with logarithmic transform works efficiently with a single image. The produced image has better contrast at both low and high ranges, i.e. has more visible details than the original one. An improved method works with high dynamic range images, useful for outdoor face images. Face expressions also modify the images’ appearance. An extended Hidden Markov Models (HMM) with a flexible encoding scheme treats images as an ensemble of horizontal and vertical strips. Each person is modeled by Joint Multiple Hidden Markov Models (JM-HMMs). This approach offers computational advantages and the good learning ability from just a single sample per class. A fast method simulated JM-HMM functionality is then derived. The new method with abstract observations and a simplified similarity measurement does not require retraining HMMs for new images or subjects. Pose invariant recognition from a single sample image per person was overcome by using the wire frame Candide face model for the synthesis of virtual views. This is one of the support functions of our face recognition system, WAWO. The extensive experiments clearly show that WAWO outperforms the state-of-the-art systems in FERET tests.

Simulated mirror displays have a promising prospect in applications, due to its capability for virtual visualization. In most existing mirror displays, cameras are placed on top of the displays and unable to capture the person in front of the display at the highest possible resolution. The lack of a direct frontal capture of the subject's face and the geometric error introduced by image warping techniques make realistic mirror image rendering a challenging problem. The objective of this thesis is to explore the use of a robotic camera in tracking the face of the subject in front of the display to obtain a high-quality image capture. Our system uses a Bislide system to control a camera for face capture, while using a separate color-depth camera for accurate face tracking. We construct an optical device in which a one-way mirror is used so that the robotic camera behind can capture the subject while the rendered images can be displayed by reflecting off the mirror from an overhead projector. A key challenge of the proposed system is the reduction of light due to the one-way mirror. The optimal 2D Wiener filter is selected to enhance the low contrast images captured by the camera.

Abstract The goal of this study was to explore preoperative evaluation of patients with intracranial tumours using magnetic resonance imaging (MRI) methods: oedema-attenuated inversion recovery (EDAIR) sequence in low-field MRI, and diffusion-weighted imaging (DWI) and resting-state functional MRI (fMRI) in high-field MRI. The aim was also to increase our knowledge about the effects of brain surgery on eloquent brain cortices using new MRI techniques. The total number of patients in these studies was 50 (24 women). Enhancement of the tumour in ten patients after intravenous administration of gadolinium-based contrast agent in low-field MRI was examined with a new sequence, EDAIR, and compared with more conventionally used partial saturation spin echo sequences. EDAIR may facilitate the perception of small enhancing lesions and is valuable in low-field imaging, where T1-based contrast is inferior to high-field imaging. DWI was performed on 25 patients in order to evaluate the potential of this imaging method to assist in differential diagnosis of intracranial tumours. It was shown that apparent diffusion coefficient values of the tumour and peritumoural oedema produced by DWI were different in benign and malignant tumours. Resting-state blood oxygen level-dependent (BOLD) fMRI was performed on eight patients and ten healthy volunteers to examine if functional sensorimotor areas in the brain could be determined without any task-related activations. It was shown that intracranial tumours do not appear to hamper visualization of the sensorimotor area in resting-state BOLD fMRI when independent component analysis is performed, and this method may be used in preoperative imaging when activation studies cannot be performed. Conventional BOLD fMRI with motor and auditory stimuli was used with seven patients as the effect of brain surgery was studied. The results suggest that resection of a tumour with preoperative oedema probably decreases pressure on the brain and makes the functional cortex transiently more easily detectable in BOLD fMRI. In conclusion, the MRI imaging methods used in this study can give valuable additional information about the tumour, specifically for preoperative imaging and planning for surgery.

京都大学 / 新制・課程博士 / 博士(人間健康科学) / 甲第23121号 / 人健博第83号 / 新制||人健||6(附属図書館) / 京都大学大学院医学研究科人間健康科学系専攻 / (主査)教授 椎名 毅, 教授 藤井 康友, 教授 平井 豊博 / 学位規則第4条第1項該当 / Doctor of Human Health Sciences / Kyoto University / DFAM

Background: Computed tomography (CT), is commonly used imaging technique in cancer patients. In CT performed for tumour staging to detect the spread of tumours, the liver is a common place for metastases. There are two types of metastases in the liver, hypo- and hyper vascularised, which generally are best seen in the venous phase and the late arterial phase. Therefore, CT of the liver often includes a triple phase contrast-enhancement protocol.   Purpose: The aim of this study was to qualitatively and quantitatively examine, through image review and attenuation measurements, respectively, the overall image quality of CT examinations performed in conjunction with positron emission tomography, (PET)/CT and as separate, stand-alone CT-studies, at the Department of Nuclear Medicine and PET centre, Uppsala University Hospital.   Method: A retrospective cross-sectional patient records review was undertaken with a descriptive approach of the qualitatively and quantitative data. 286 examinations were included in the study.     Result: The examinations were of high-quality in terms of contrast-enhancement technique, 183/190 (96,3%) of late arterial phases and 211/286 (74%) of venous phases. Some characteristic differences in the contrast-enhancement of the liver and the metastases were found in the different CT phases, allowing for division into four separate categories based on their respective characteristics.   Conclusion: In summary, the performed CT examinations were in majority performed in the right phase. The image quality of Contrast-to-noise, (CNR) and Signal-to-noise (SNR) were satisfactory even if the CNR could be higher for a better distinguish and detected small structures in the images. / Bakgrund: Datortomografi (DT), är den vanligaste avbildningstekniken för cancerpatienter. Vid DT för utredning av tumörspridning är levern en vanlig plats för metastaser. Det finns två typer av metastaser i levern, hypo- och hypervaskulära, som vanligen ses bäst i venfas respektive i sen artärfas. DT-undersökningarna av levern inkluderar därför ofta ett trippelfas-undersöknings-protokoll. Syfte: Syftet med denna studie var att kvalitativt och kvantitativt undersöka, genom bildgranskning respektive attenueringsmätningar, den totala bildkvaliteten för DT-undersökningar utförda i samband med positronemissionstomografi (PET) / DT och som separata, fristående CT-studier, vid Institutionen för nuklearmedicin och PET-center, Uppsala universitetssjukhus.   Metod: En retrospektiv tvärsnittsstudie med patientjournaler där en beskrivande metod användes för att beskriva den kvalitativt och kvantitativt datan. 286 DT-undersökningar granskades och inkluderades i studien. Resultat: Undersökningarna utfördes med DT i samband med PET/ DT, och även som separata DT undersökningar, var av hög kvalité vad gäller kontrastförstärkningsteknik varav 183/190 (96,3%) av sena artärfasundersökningarna och 211/286 (74%) av venfasundersökningarna erhöll maximal kvalitetspoäng. Vissa karakteristiska skillnader i kontrastförstärkningen av levern gentemot metastaserna hittades för de mycket och dåligt vaskulariserade metastaserna i de olika CT-faserna, vilket möjliggjorde uppdelning i fyra separata kategorier baserat på deras respektive egenskaper.   Slutsats: Sammanfattningsvis utfördes de utförda DT-undersökningarna i majoritet i rätt fas. Bildkvaliteten på Kontrast-till-brus, (CNR) och Signal-till-brus (SNR) var tillfredsställande även om CNR kunde vara högre för att bättre skilja och upptäcka små strukturer i bilderna.