Automatic detection of motion blur in intravital video microscopy image sequences via directional statistics of log-Gabor energy maps

Abstract

Intravital microscopy is an important experimental tool for the study of cellular and molecular mechanisms of the leukocyte-endothelial interactions in the microcirculation of various tissues and in different inflammatory conditions of in vivo specimens. However, due to the limited control over the conditions of the image acquisition, motion blur and artifacts, resulting mainly from the heartbeat and respiratory movements of the in vivo specimen, will very often be present. This problem can significantly undermine the results of either visual or computerized analysis of the acquired video images. Since only a fraction of the total number of images are usually corrupted by severe motion blur, it is necessary to have a procedure to automatically identify such images in the video for either further restoration or removal. This paper proposes a new technique for the detection of motion blur in intravital video microscopy based on directional statistics of local energy maps computed using a bank of 2D log-Gabor filters. Quantitative assessment using both artificially corrupted images and real microscopy data were conducted to test the effectiveness of the proposed method. Results showed an area under the receiver operating characteristic curve (AUC) of 0.95 (AUC = 0.95; 95 % CI 0.93-0.97) when tested on 329 video images visually ranked by four observers.

Automated Detection of Uninformative Frames in Pulmonary Optical Endomicroscopy

“…(16) Note that if the variables u ∈ PC1 were actually independent, identically distributed (i.i.d.) variables following (12), the selected variables in PC1 sub would be distributed according to (14). However, since the Gaussian mixture (12) was an approximation of the actual distribution of u ∈ PC1, the distribution (14) was therefore also an approximation of the distribution of u ∈ PC1 sub .…”

“…variables following (12), the selected variables in PC1 sub would be distributed according to (14). However, since the Gaussian mixture (12) was an approximation of the actual distribution of u ∈ PC1, the distribution (14) was therefore also an approximation of the distribution of u ∈ PC1 sub . Nevertheless, as suggested by the results in Section V, in practice this approximation was accurate enough, leading to satisfactory results in terms of uninformative frame detection.…”

“…There has been considerable research in the suppression of noise [12] as well as for the detection of motion artefacts for a range of imaging modalities, including, but not limited to, aerial images [13], microscopy [14], medical images [15], as well as other digital photography images [16][17][18]. However, most such studies focus on the detection of motion-blurred regions within a frame with the intention to compensate for it through some image enhancement algorithm.…”

Detecção de leucócitos em imagens de vídeo de microscopia intravital usando a técnica de congruência de fase

“…Os quadros com excessivo borramento ou distorções foram identificados pela técnica proposta em Ferrari et al [7] e removidos do vídeo. A remoção dos quadros só foi possível pois, para a aquisição do vídeo, foi utilizada uma alta taxa de amostragem.…”

Time-Space Fourier κώ Filter for Motion Artifacts Compensation during Transcranial Fluorescence Brain Imaging

“…Current Image Quality Assessments (IQAs) are difficult to interpret in the particular context of fast dynamic intensity associated to versatile appearance of structures in the region of interest. Quantitative assessments using both artificially corrupted images and real microscopy [18] or using a dissimilarity index calculated between a reference image and each image in the sequence in order to detect the presence of artifacts have been recently proposed in intra-vital video microscopy [19]. We introduce a useful temporal code colorcoding method to perform a quick evaluation of the image quality of the entire sequence in Section 4.…”