Multimodal Imaging Analysis Research Articles

Simultaneous BOLD-fMRI and constant infusion FDG-PET data of the resting human brain

Simultaneous [18 F]-fluorodeoxyglucose positron emission tomography and functional magnetic resonance imaging (FDG-PET/fMRI) provides the capability to image two sources of energetic dynamics in the brain – cerebral glucose uptake and the cerebrovascular haemodynamic response. Resting-state fMRI connectivity has been enormously useful for characterising interactions between distributed brain regions in humans. Metabolic connectivity has recently emerged as a complementary measure to investigate brain network dynamics. Functional PET (fPET) is a new approach for measuring FDG uptake with high temporal resolution and has recently shown promise for assessing the dynamics of neural metabolism. Simultaneous fMRI/fPET is a relatively new hybrid imaging modality, with only a few biomedical imaging research facilities able to acquire FDG PET and BOLD fMRI data simultaneously. We present data for n = 27 healthy young adults (18–20 yrs) who underwent a 95-min simultaneous fMRI/fPET scan while resting with their eyes open. This dataset provides significant re-use value to understand the neural dynamics of glucose metabolism and the haemodynamic response, the synchrony, and interaction between these measures, and the development of new single- and multi-modality image preparation and analysis procedures.

Localized Prediction of Glutamate from Whole-Brain Functional Connectivity of the Pregenual Anterior Cingulate Cortex.

Local measures of neurotransmitters provide crucial insights into neurobiological changes underlying altered functional connectivity in psychiatric disorders. However, noninvasive neuroimaging techniques such as magnetic resonance spectroscopy (MRS) may cover anatomically and functionally distinct areas, such as p32 and p24 of the pregenual anterior cingulate cortex (pgACC). Here, we aimed to overcome this low spatial specificity of MRS by predicting local glutamate and GABA based on functional characteristics and neuroanatomy in a sample of 88 human participants (35 females), using complementary machine learning approaches. Functional connectivity profiles of pgACC area p32 predicted pgACC glutamate better than chance (R2 = 0.324) and explained more variance compared with area p24 using both elastic net and partial least-squares regression. In contrast, GABA could not be robustly predicted. To summarize, machine learning helps exploit the high resolution of fMRI to improve the interpretation of local neurometabolism. Our augmented multimodal imaging analysis can deliver novel insights into neurobiology by using complementary information.SIGNIFICANCE STATEMENT Magnetic resonance spectroscopy (MRS) measures local glutamate and GABA noninvasively. However, conventional MRS requires large voxels compared with fMRI, because of its inherently low signal-to-noise ratio. Consequently, a single MRS voxel may cover areas with distinct cytoarchitecture. In the largest multimodal 7 tesla machine learning study to date, we overcome this limitation by capitalizing on the spatial resolution of fMRI to predict local neurotransmitters in the PFC. Critically, we found that prefrontal glutamate could be robustly and exclusively predicted from the functional connectivity fingerprint of one of two anatomically and functionally defined areas that form the pregenual anterior cingulate cortex. Our approach provides greater spatial specificity on neurotransmitter levels, potentially improving the understanding of altered functional connectivity in mental disorders.

Atypical findings in unilateral Acute Idiopathic Maculopathy: a case report

Acute idiopathic maculopathy (AIM) is a condition reported to occur in young adults, which is classically characterized by the development of a unilateral serous macular detachment that commonly follows a flu-like illness. A 27-year-old female patient complained of diminution of vision in her left eye, of acute onset and progressive course for 2 weeks. She had a history of a flu-like condition 2 weeks prior. Following clinical examination, multimodal imaging analysis, and laboratory investigations, she was diagnosed with unilateral AIM, associated with atypical findings that were not previously reported to occur with AIM, including cystoid macular edema, flame shaped retinal hemorrhages, extensive hard exudates, and juxta-foveal yellowish intraretinal lesions. This may have been due to the more severe inflammation and concomitant phlebitis that was associated with this case compared to previously reported AIM cases.

Incidence and Natural History of Retinochoroidal Neovascularization in Enhanced S-Cone Syndrome

We examined the incidence and natural history of macular retinochoroidal neovascularization (RCN) in enhanced S-cone syndrome (ESCS). Retrospective case series. This single-center study included 14 of 93 patients with ESCS who had signs of active or inactive RCN in ≥1 eye. We conducted multimodal retinal imaging, full-field electroretinography, and molecular genetic analysis of NR2E3 gene. Our main outcome measures included the cumulative incidence of RCN in ESCS, type of RCN, and mode of evolution of RCN. Fourteen (15.1%) of 93 patients with ESCS had RCN in ≥1 eye at 2 to 27 years of age. All 22 RCNs (21 eyes of 14 patients) were macular. Twelve of the RCNs were active with exudates/hemorrhages. Of these, 5 appeared de novo in a subretinal location, with photographic evidence of no pre-existing lesions. The latter were compatible with type 3 neovascularization or retinal angiomatous proliferation and subsequently evolved into unifocal fibrotic nodules. The remaining active lesions all had some degree of pre-existing fibrosis and remained stable. Ten inactive fibrotic nodules, identical to end-stage de novo lesions, were found and were presumed to represent healed RCNs. RCN, a treatable condition, may occur as early as 2 years of age and may be much more common in patients with ESCS than previously estimated. It may be the primary cause of the unifocal submacular fibrosis that is commonly observed in this condition. Additional research is needed to establish the pathogenesis of RCN in patients with ESCS and its optimal management.

Breathing new life into neurotoxic-based monkey models of Parkinson's disease to study the complex biological interplay between serotonin and dopamine.

Numerous clinical studies have shown that the serotonergic system also degenerates in patients with Parkinson's disease. The causal role of this impairment in Parkinson's symptomatology and the response to treatment remains to be refined, in particular thanks to approaches allowing the two components DA and 5-HT to be isolated if possible. We have developed a macaque monkey model of Parkinson's disease exhibiting a double lesion (dopaminergic and serotonergic) thanks to the sequential use of MPTP (1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine) and MDMA (3,4-methylenedioxy-N-methamphetamine) (or MDMA prior MPTP). We characterized this monkey model by multimodal imaging (PET, positron emission tomography with several radiotracers; DTI, diffusion tensor imaging), behavioral assessments (parkinsonism, dyskinesia, neuropsychiatric-like behavior) and post-mortem analysis (with DA and 5-HT markers). When administrated after MPTP, MDMA damaged the 5-HT presynaptic system without affecting the remaining DA neurons. The lesion of 5-HT fibers induced by MDMA altered rigidity and prevented dyskinesia and neuropsychiatric-like symptoms induced by levodopa therapy in MPTP-treated animals. Interestingly also, prior MDMA administration aggravates the parkinsonian deficits and associated DA injury. Dystonic postures, action tremor and global spontaneous activities were significantly affected. All together, these data clearly indicate that late or early lesions of the 5-HT system have a differential impact on parkinsonian symptoms in the macaque model of Parkinson's disease. Whether MDMA has an impact on neuropsychiatric-like symptoms such as apathy, anxiety, depression remains to be addressed. Despite its limitations, this toxin-based double-lesioned monkey model takes on its full meaning and provides material for the experimental study of the heterogeneity of patients.

Research Advances and Obstacles of CT-based Radiomics in Diagnosis and Treatment of Lung Cancer

影像组学是一种基于多模态医学影像处理分析的技术，该技术能够基于高性能计算机及算法从目前普遍使用的计算机断层扫描（computed tomography, CT）、磁共振图像（magnetic resonance imaging, MRI）和正电子发射/断层图像（positron emission tomography/computed tomography, PET/CT）中自动提取海量数据进行分析，对疾病的早期诊断、良恶性肿瘤鉴别、疾病治疗全程管理，个体化精准治疗等需求提供更多有价值信息。近年来，许多研究表明基于CT的影像组学技术在肺癌的早期诊断、基因表型预测、疗效预测及预后评估均有良好的应用价值，且影像学检查具有无创、经济、可重复等优势。其对临床的指导价值已有所展露，在肺癌的个体化、精准化治疗和研究方面具有较大价值，但是，影像组学特征的重复性和一致性问题以及在肺部肿瘤图像提取中的特征筛选还需进一步研究。

IDENTIFICATION AND CLASSIFICATION OF MACULAR MORPHOLOGIC BIOMARKERS RELATED TO VISUAL ACUITY IN RADIATION MACULOPATHY: A Multimodal Imaging Study.

To identify and classify, by a multimodal imaging approach, the most relevant macular morphologic biomarkers related to visual acuity in patients affected by radiation maculopathy secondary to brachytherapy. Fifty-one consecutive patients previously treated with Iodine-125 brachytherapy because of uveal melanoma were enrolled. Each patient underwent full ophthalmologic examination including best-corrected visual acuity and multimodal macular imaging analysis. Macular morphological parameters were processed by a stepwise selection analysis. Three macular parameters were identified as the most relevant macular morphologic biomarkers of poor visual acuity: the vertical thickness of the thickest macular cyst (P = 0.0001), the presence of foveal inner segment/outer segment (IS/OS) layer disruption (P = 0.0054), and the presence of foveal retinal pigment epithelium atrophy (0.0884). The intergrader agreement for these morphologic biomarkers was 0.98, 0.92, and 0.92, respectively (interclass correlation coefficient). The vertical thickness of the thickest macular cyst, the presence of foveal retinal pigment epithelium atrophy, and IS/OS layer disruption can be used to clinically characterize radiation maculopathy. These parameters allow for separation of the edematous component of radiation maculopathy, which is potentially treatable in early disease stages, from late onset atrophic components, which are theoretically irreversible.

Functional connectome contractions in temporal lobe epilepsy: Microstructural underpinnings and predictors of surgical outcome.

Temporal lobe epilepsy (TLE) is the most common drug-resistant epilepsy in adults. Although it is commonly related to hippocampal pathology, increasing evidence suggests structural changes beyond the mesiotemporal lobe. Functional anomalies and their link to underlying structural alterations, however, remain incompletely understood. We studied 30 drug-resistant TLE patients and 57 healthy controls using multimodal magnetic resonance imaging (MRI) analyses. All patients had histologically verified hippocampal sclerosis and underwent postoperative imaging to outline the extent of their surgical resection. Our analysis leveraged a novel resting-state functional MRI framework that parameterizes functional connectivity distance, consolidating topological and physical properties of macroscale brain networks. Functional findings were integrated with morphological and microstructural metrics, and utility for surgical outcome prediction was assessed using machine learning techniques. Compared to controls, TLE patients showed connectivity distance reductions in temporoinsular and prefrontal networks, indicating topological segregation of functional networks. Testing for morphological and microstructural associations, we observed that functional connectivity contractions occurred independently from TLE-related cortical atrophy but were mediated by microstructural changes in the underlying white matter. Following our imaging study, all patients underwent an anterior temporal lobectomy as a treatment of their seizures, and postsurgical seizure outcome was determined at a follow-up at least 1year after surgery. Using a regularized supervised machine learning paradigm with fivefold cross-validation, we demonstrated that patient-specific functional anomalies predicted postsurgical seizure outcome with 76±4% accuracy, outperforming classifiers operating on clinical and structural imaging features. Our findings suggest connectivity distance contractions as a macroscale substrate of TLE. Functional topological isolation may represent a microstructurally mediated network mechanism that tilts the balance toward epileptogenesis in affected networks and that may assist in patient-specific surgical prognostication.

Application of chemometric methods to the analysis of multimodal chemical images of biological tissues.

Current histology techniques, such as tissue staining or histochemistry protocols, provide very limited chemical information about the tissues. Chemical imaging technologies such as infrared, Raman, and mass spectrometry imaging, are powerful analytical techniques with a huge potential in describing the chemical composition of sample surfaces. In this work, three images of the same tissue slice using matrix-assisted laser desorption/ionization (MALDI) mass spectrometry, infrared microspectroscopy, and an RGB picture from a conventional hematoxylin/eosin (H/E) staining are simultaneously analyzed. These fused images were analyzed by multivariate curve resolution-alternating least squares (MCR-ALS), which provided, for each component, its distribution within the tissue surface, its IR spectrum fingerprint, its characteristic mass values, and the contribution of the RGB channels of the H/E staining. Compared with the individual analysis of each of the images alone, the fusion of the three images showed the relationship between the different types of chemical/biological information and enabled a better interpretation of the tissue under study. In addition, the least-squares projection of the MCR-ALS resolved spectra of components at low spatial resolution onto the IR and RBG images at high spatial resolution, provided a better delimitation of the sample constituents on the image, giving a more precise description of their distribution on the investigated tissue. The application of this procedure can be of interest in different research areas in which a good description of the spatial distribution of the chemical constituents of the samples is needed, such as in biomedicine, food, or environmental research.

SlicerDMRI: Diffusion MRI and Tractography Research Software for Brain Cancer Surgery Planning and Visualization.

PURPOSEWe present SlicerDMRI, an open-source software suite that enables research using diffusion magnetic resonance imaging (dMRI), the only modality that can map the white matter connections of the living human brain. SlicerDMRI enables analysis and visualization of dMRI data and is aimed at the needs of clinical research users. SlicerDMRI is built upon and deeply integrated with 3D Slicer, a National Institutes of Health–supported open-source platform for medical image informatics, image processing, and three-dimensional visualization. Integration with 3D Slicer provides many features of interest to cancer researchers, such as real-time integration with neuronavigation equipment, intraoperative imaging modalities, and multimodal data fusion. One key application of SlicerDMRI is in neurosurgery research, where brain mapping using dMRI can provide patient-specific maps of critical brain connections as well as insight into the tissue microstructure that surrounds brain tumors.PATIENTS AND METHODSIn this article, we focus on a demonstration of SlicerDMRI as an informatics tool to enable end-to-end dMRI analyses in two retrospective imaging data sets from patients with high-grade glioma. Analyses demonstrated here include conventional diffusion tensor analysis, advanced multifiber tractography, automated identification of critical fiber tracts, and integration of multimodal imagery with dMRI.RESULTSWe illustrate the ability of SlicerDMRI to perform both conventional and advanced dMRI analyses as well as to enable multimodal image analysis and visualization. We provide an overview of the clinical rationale for each analysis along with pointers to the SlicerDMRI tools used in each.CONCLUSIONSlicerDMRI provides open-source and clinician-accessible research software tools for dMRI analysis. SlicerDMRI is available for easy automated installation through the 3D Slicer Extension Manager.

Multimodal Image Analysis for Assessing Multiple Sclerosis and Future Prospects Powered by Artificial Intelligence

The purpose of this paper is to serve as a template for greater understanding for the practicing radiologist about key steps to perform multimodality computer analysis of MRI images, specifically in multiple sclerosis patients. With this understanding, radiologists will be better equipped about how best to process and analyze MRI imaging data and obtain accurate quantitative information for MS patient evaluation. A secondary intent of this article is to improve radiologist understanding of how artificial intelligence will be employed in the future for better patient stratification, and for evaluation of response to therapy in both clinical care and drug trials.

Clinical characteristics of asymptomatic Terson syndrome in the patients with aneurysmal subarachnoid hemorrhage.

To investigate clinical characteristics of asymptomatic Terson syndrome and its clinical impact in patients with aneurysmal subarachnoid hemorrhage (SAH). This retrospective, interventional study included 31 patients with aneurysmal SAH, and the medical records were reviewed. In addition to baseline characteristics of the study population such as age, sex, and underlying medical history, multi-modal imaging analysis, including fluorescein angiography (FA), spectral domain optical coherence tomography (SD-OCT), were also reviewed. Glasgow Coma Scale (GCS), Hunt-Hess (HH) grade, and Fisher scale at the time of admission, and functional outcome by using modified Rankin Scale (mRS) at 6mo were compared. Of the 31 patients, 10 patients (32.3%) were diagnosed with Terson syndrome. All the patients with Terson syndrome did not report visual symptoms at the time of ophthalmologic screening. FA showed microvascular changes of retinal capillaries and varying degrees of disc leakage. SD-OCT allowed intuitive anatomical localization of multi-layered retinal hemorrhages and assessment of ellipsoid zone integrity. The patients with Terson syndrome showed significantly worse GCS (P=0.047) and HH grade (P=0.025) than those without, except Ficher scale (P=0.385). There was no significant difference in the mRS (P=0.250) at 6mo. Among baseline factors, the HH grade was the only significant factor associated with Terson syndrome (B=1.079, P=0.016). In our study, 32.3% of the patients have Terson syndrome without visual symptoms. The baseline HH grade is significantly correlated with Terson syndrome, and there is no significant difference in the functional outcome between the patients with and without Terson syndrome. Terson syndrome may develop without any visual symptoms as shown in our study, and ophthalmologic screening may be recommended to prevent further visual deterioration especially in the patients with poor HH grade at the time of aneurysmal SAH.

Pulmonary Vein Enlargement as an Independent Predictor for New-Onset Atrial Fibrillation.

Pulmonary vein (PV) enlargement is associated with atrial fibrillation (AF). However, the predictive value of PV volume for new-onset AF has not been determined. We retrospectively assessed and enrolled non-AF subjects who underwent echocardiography and cardiac CT angiography (CCTA) around the same time and evaluated the development of AF longitudinally. PV volume was assessed by estimating the three-dimensional CCTA-derived mid-diastolic PV volume from the ostium to tertiary branches. Overall, 1105 subjects were enrolled. Among them, 29 developed AF during a mean follow-up of 4.28 ± 3.08 years after baseline CCTA and echocardiography. The AF group had a higher proportion of older aged subjects, a higher ratio of early mitral flow velocity (E) to early mitral annular tissue velocity (Em), higher Em, and larger left atrial (LAVI) and PV (PVVI) volume indices. PVVI was independently associated with male sex, left ventricular dimension, E/Em and LAVI. AF incidence increased markedly across each baseline PVVI tertile (2.2%, 5.1%, and 10.8%). In the multivariate Cox model, increased PVVI was independently associated with new-onset AF (hazard ratio (HR) = 5.401, 4.931–6.193, p = 0.007). Based on the analysis of multimodal cardiac imaging, our results provide mechanistic insights into PV remodeling and its potential role as a link between diastolic dysfunction and developing AF.

Multimodal Retinal Image Analysis via Deep Learning for the Diagnosis of Intermediate Dry Age-Related Macular Degeneration: A Feasibility Study.

Results The CNN trained using OCT alone showed a diagnostic accuracy of 94%, whilst the OCT-A trained CNN resulted in an accuracy of 91%. When multiple modalities were combined, the CNN accuracy increased to 96% in the AMD cohort. Conclusions Here we demonstrate that superior diagnostic accuracy can be achieved when deep learning is combined with multimodal image analysis.

BRAHMA: Population specific T1, T2, and FLAIR weighted brain templates and their impact in structural and functional imaging studies

Differences in brain morphology across population groups necessitate creation of population-specific Magnetic Resonance Imaging (MRI) brain templates for interpretation of neuroimaging data. Variations in the neuroanatomy in a genetically heterogeneous population make the development of a population-specific brain template for the Indian subcontinent imperative. A dataset of high-resolution 3D T1, T2-weighted, and FLAIR images acquired from a group of 113 volunteers (M/F - 56/57, mean age-28.96 ± 7.80 years) are used to construct T1, T2-weighted, and FLAIR templates, collectively referred to as Indian Brain Template, “BRAHMA”. A processing pipeline is developed and implemented in a MATLAB based toolbox for template construction and generation of tissue probability maps and segmentation atlases, with additional labels for deep brain regions such as the Substantia Nigra generated from the T2-weighted and FLAIR templates. The use of BRAHMA template for analysis of structural and functional neuroimaging data obtained from Indian participants, provides improved accuracy with statistically significant results over that obtained using the ICBM-152 (International Consortium for Brain Mapping) template. Our results indicate that segmentations generated on structural images are closer in volume to those obtained from registration to the BRAHMA template than to the ICBM-152. Furthermore, functional MRI data obtained for Working Memory and Finger Tapping paradigms processed using the BRAHMA template show a significantly higher percentage of the activation area than ICBM-152 in relevant brain regions, i.e. the left middle frontal gyrus, and the left and right precentral gyri, respectively. The availability of different image contrasts, tissue maps, and segmentation atlases makes the BRAHMA template a comprehensive tool for multi-modal image analysis in laboratory and clinical settings.

Unilateral cone dysfunction with asymmetric maculopathy - Clinical features, multimodal imaging and genetic analysis of a novel phenotype.

Unilateral cone dysfunction with asymmetric maculopathy - Clinical features, multimodal imaging and genetic analysis of a novel phenotype.

3D Slicer Craniomaxillofacial Modules Support Patient-Specific Decision-Making for Personalized Healthcare in Dental Research.

The biggest challenge to improve the diagnosis and therapies of Craniomaxillofacial conditions is to translate algorithms and software developments towards the creation of holistic patient models. A complete picture of the individual patient for treatment planning and personalized healthcare requires a compilation of clinician-friendly algorithms to provide minimally invasive diagnostic techniques with multimodal image integration and analysis. We describe here the implementation of the open-source Craniomaxillofacial module of the 3D Slicer software, as well as its clinical applications. This paper proposes data management approaches for multisource data extraction, registration, visualization, and quantification. These applications integrate medical images with clinical and biological data analytics, user studies, and other heterogeneous data.

Training data independent image registration using generative adversarial networks and domain adaptation

Medical image registration is an important task in automated analysis of multimodal images and temporal data involving multiple patient visits. Conventional approaches, although useful for different image types, are time consuming. Of late, deep learning (DL) based image registration methods have been proposed that outperform traditional methods in terms of accuracy and time. However, DL based methods are heavily dependent on training data and do not generalize well when presented with images of different scanners or anatomies. We present a DL based approach that can perform medical image registration of one image type despite being trained with images of a different type. This is achieved by unsupervised domain adaptation in the registration process and allows for easier application to different datasets without extensive retraining. To achieve our objective we train a network that transforms the given input image pair to a latent feature space vector using autoencoders. The resultant encoded feature space is used to generate the registered images with the help of generative adversarial networks (GANs). This feature transformation ensures greater invariance to the input image type. Experiments on chest X-ray, retinal and brain MR images show that our method, trained on one dataset gives better registration performance for other datasets, outperforming conventional methods that do not incorporate domain adaptation.

Multimodal Image Fusion in Clinical Research

With the advancement in medical imaging, far reaching changes are perceptible in clinical analysis. Most of the diagnosed medical evaluation is a resultant of imaging or it is in close synchronization with imaging techniques. This promotes the need to closely read, evaluate and collaborate images. Medical image fusion is a technique to collaborate the clinical images acquired from one or many modalities. Multimodal image analysis during fusion well capitalizes the strength of each medical modality. Incorporating the features from multimodal input images thus holds added potential to abet better-quality diagnosis. Medical image fusion is an intricate task specially when high quality fused image, possessing all relevant information and reasonable operating speed is aimed. Many efforts have been undertaken in this field resulting in diverse research approaches. Image fusion can be performed using medical images obtained from single modality or from multiple modalities. This paper has been designed collaborating the work based on multimodal medical images in multiscale image fusion domain. When the same region, organ or a tissue is captured from various different perceptions, complimentary information is maximized and diagnostic value is reinforced. The fusion framework using Mexican Hat wavelet is proposed using adaptive median filtering detailing each executable fusion block. The Fusion techniques, pre and post processing aspects and evaluation mechanisms are illustrated from literature. The drifts of researchers from single processing to multiple processing hybrid techniques are discussed. The medical modality aspects are detailed. These may provide as a valuable reference to understand the image fusion trade-offs comprehensively with future viabilities.

Comparison of feature point detectors for multimodal image registration in plant phenotyping.

With the introduction of multi-camera systems in modern plant phenotyping new opportunities for combined multimodal image analysis emerge. Visible light (VIS), fluorescence (FLU) and near-infrared images enable scientists to study different plant traits based on optical appearance, biochemical composition and nutrition status. A straightforward analysis of high-throughput image data is hampered by a number of natural and technical factors including large variability of plant appearance, inhomogeneous illumination, shadows and reflections in the background regions. Consequently, automated segmentation of plant images represents a big challenge and often requires an extensive human-machine interaction. Combined analysis of different image modalities may enable automatisation of plant segmentation in “difficult” image modalities such as VIS images by utilising the results of segmentation of image modalities that exhibit higher contrast between plant and background, i.e. FLU images. For efficient segmentation and detection of diverse plant structures (i.e. leaf tips, flowers), image registration techniques based on feature point (FP) matching are of particular interest. However, finding reliable feature points and point pairs for differently structured plant species in multimodal images can be challenging. To address this task in a general manner, different feature point detectors should be considered. Here, a comparison of seven different feature point detectors for automated registration of VIS and FLU plant images is performed. Our experimental results show that straightforward image registration using FP detectors is prone to errors due to too large structural difference between FLU and VIS modalities. We show that structural image enhancement such as background filtering and edge image transformation significantly improves performance of FP algorithms. To overcome the limitations of single FP detectors, combination of different FP methods is suggested. We demonstrate application of our enhanced FP approach for automated registration of a large amount of FLU/VIS images of developing plant species acquired from high-throughput phenotyping experiments.