Multiscale Hessian filter-based segmentation and quantification method for photoacoustic microangiography Download: 1289次
Imaging of microcirculatory
Photoacoustic microangiography provides the direct visualization of blood vessels[9,10]. Usually these images are interpreted qualitatively. Therefore, a quantitative method for characterizing blood vessels would have several clinical applications. Previous methods include measuring blood vessel diameters[11], the flow velocity[12], and the maximum distance to the nearest blood vessel[13]. It is worth noting that all of these parameters are intensity information. A parameter that can describe the vessel tortuosity would be more beneficial. For example, a change in retinal vessels is an early indicator of coronary heart disease[14] and stroke[15]. Vascular remodeling in which vessel tortuosity plays an important role has also been of interest in several fields[16]. Thus, four measurement parameters are considered to give a more complete description.
In order to quantify the morphology of blood vessels, a segmentation algorithm is needed first. The segmentation algorithm returns a binary map of locations of vessels. Numerous segmentation methods have been proposed and the structure or intensity information is explored. The simplest one is the adaptive threshold algorithm that is based on the intensity. However, the intensity-based techniques are very sensitive to the threshold parameter selection while lack sensitivity to the morphology of the blood vessels, which often leads to over segmentation.
By exploring both the shape and direction of vessels, the Hessian filter is a good fit for multisize blood vessels[1719" target="_self" style="display: inline;">–
In this Letter, a multiscale Hessian filter-based algorithm is proposed. The limitation of the Hessian filter has been analyzed and corrected by a local adaptive threshold algorithm. Additionally, the segmented binary image is utilized to acquire four measurement parameters to further quantify the blood vessels. Finally, the segmentation and quantification method is valid on the whole and a small area of photoacoustic microangiography to identify different tissue characteristics.
The first step of the proposed method is the segmentation progress. The algorithm we considered is based on the multiscale Hessian filter. The multiscale Hessian filter proposed by Frangi[20] has been widely used in many applications[2123" target="_self" style="display: inline;">–
Figure
Moreover, in order to identify the blood vessels from the background, a third ratio is introduced that is defined as
Therefore, the vessel function at scale
By using the multiscale Hessian filter, different size of blood vessels could be extracted. However, the method is very sensitive to the maximum scale. Figures
Fig. 2. Sensitivity of the Hessian filter to the maximum scale: (a–c) segmentation results obtained by the multiscale Hessian filter using maximum scale 1, 7, 10, and (d–f) is the corresponding vessel diameter quantification map.
In order to minimize the sensitivity to the maximum scale parameter, a local adaptive threshold method is incorporated. It is utilized in parallel with the multiscale Hessian filter. The final result is acquired by compounding these two results with a weighted average scheme,
The performance of the multiscale Hessian-based segmentation algorithm is shown in Fig.
Fig. 3. Performance of the multiscale Hessian-based segmentation algorithm: (a) the original photoacoustic microangiography, (b) segmentation result obtained by the local adaptive threshold method, (c) segmentation result obtained by the Hessian filter, (d) segmentation result obtained by the proposed method with an over-large maximum scale, (e) segmentation result obtained by the proposed method with an appropriate maximum scale, and (f) the corresponding vessel diameter quantification map of (e).
Based on the better-segmented binary map, four measurement parameters (fractal dimension, vessel length fraction, vessel density, and vessel diameter) will be quantified.
The vessel diameter is the most commonly used parameter. The distance transform of a blood vessel is the minimum number of pixels between each foreground pixel to the boundary of the vessel[25]. The results should have a maximum value on the vessel centerline. The exact vessel diameter can be measured after correcting by the spatial size of each pixel. Here, the distance transform result is used to represent the quantified vessel diameter. Both Figs.
Vessel density and vessel length fraction are parameters that represent a relative value of the total area occupied by the vessels and the total length of the vessels, respectively[26]. The vessel density can be acquired directly on the segmented image. It is calculated as the number of the white pixels, which represent the total area covered by the blood vessels, divided by the total number of images, which represents the total area of the imaging area. The vessel density value quantified for Fig.
For the vessel length fraction, skeletal images that represent the total length of the blood vessels are needed. The skeletonization process consists of iteratively deleting the pixels in the outer boundary of the segments until a single pixel width line is obtained[27]. In other words, the skeletal image can represent the centerline of the vessel. Figure
Fractal dimension is a parameter to characterize a self-similar image[28]. A fractal dimension is a value that gives an indication of how an image fills space into smaller scales. Here, the parameter is utilized to quantify the vessel turtuosity. It has been applied in diverse areas of medicine to describe complex biological structures such as branching patterns of the retina, coronary, and pulmonary arterioles[29]. It has also been used to quantify the fractal distribution of scatters in tissues, the parafoveal capillary network, and optical coherence tomography images of arteries[30].
Although the fractal dimension can be computed both on the segmented image and the skeleton map, the result acquired from the skeleton map is more sensitive to changes of the vessels[31]. Thus, in this Letter, the fractal dimension are all calculated on the skeletal image. A box counting method is utilized to calculate the fractal dimension. It is a method of estimating the fractal dimension from structures that are not perfectly self-similar[32]. More importantly, the box counting method can also be used in the quantification of the fractal dimension on a small area. The fractal dimension value quantified for Fig.
In the study of several microvascular phenomena, such as angiogenesis (growth of new blood vessels), it is important to quantify small areas of tissue. It can help us find the location of the diseased tissues in the region of interest (ROI). For example, regions close to tumors may present angiogenic blood vessels (higher tortuosity and fractal dimension) compared to the healthy surrounding blood vessels.
For the small area quantification, the image is cropped to create smaller ones. Therefore, the vessel length fraction, vessel density, and fractal dimension would be calculated over the smaller image. Additionally, there is no extra change to quantify the vessel diameter on small areas.
For Fig.
The vessel length fraction, vessel density, and fractal dimension from the photoacoustic microangiography [Fig.
Fig. 5. Small area quantification maps: (a) vessel length fraction map, (b) vessel density map, and (c) fractal dimension map.
Two ROIs have been selected as shown in Fig.
Fig. 7. Mean and standard deviations of the measurement parameters within the two ROIs: (a) vessel diameter, (b) vessel length fraction, (c) vessel density, (d) fractal dimension.
In general, the large blood vessels regions has larger diameter values than the smaller ones. Figure
In conclusion, a multiscale Hessian-based segmentation and quantification method is proposed for photoacoustic microangiography. In the proposed segmentation algorithm, the blurring and enlargement that are limitations of the Hessian filter are corrected. The results of the algorithm can be utilized to get more effective measurement parameters. The vessel diameter, vessel density, vessel length density, and fractal dimension are quantified to give both intensity and tortuous information of the blood vessels. Moreover, the segmentation and quantification method is applied on a small area within the photoacoustic microangiography to give a quantified color map. This is very important to properly characterize different ROIs within an image. In the future, the proposed method for a small area could be used to monitor the morphological changes in regions close or far away from a diseased region, such as a burn or cancer.
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[8]
[9]
[10]
[11]
[12]
[13]
[14]
[15]
[16]
[17]
[18]
[19]
[20]
[21]
[22]
[23]
[24]
[25]
[26]
[27]
[28]
[29]
[30]
[31]
Ting Liu, Mingjian Sun, Naizhang Feng, Zhenghua Wu, Yi Shen. Multiscale Hessian filter-based segmentation and quantification method for photoacoustic microangiography[J]. Chinese Optics Letters, 2015, 13(9): 091701.