The detection and analysis of the solar coronal holes (CHs) is an important
field of study in the domain of solar physics. Mainly, it is required for the
proper prediction of the geomagnetic storms which directly or indirectly affect
various space and ground-based systems. For the detection of CHs till date, the
solar scientist depends on manual hand-drawn approaches. However, with the
advancement of image processing technologies, some automated image segmentation
methods have been used for the detection of CHs. In-spite of this, fast and
accurate detection of CHs are till a major issues. Here in this work, a novel
quantum computing-based fast fuzzy c-mean technique has been developed for fast
detection of the CHs region. The task has been carried out in two stages, in
first stage the solar image has been segmented using a quantum computing based
fast fuzzy c-mean (QCFFCM) and in the later stage the CHs has been extracted
out from the segmented image based on image morphological operation. In the
work, quantum computing has been used to optimize the cost function of the fast
fuzzy c-mean (FFCM) algorithm, where quantum approximate optimization algorithm
(QAOA) has been used to optimize the quadratic part of the cost function. The
proposed method has been tested for 193 \AA{} SDO/AIA full-disk solar image
datasets and has been compared with the existing techniques. The outcome shows
the comparable performance of the proposed method with the existing one within
a very lesser time.

本研究提出了一种基于量子计算的快速模糊 C 均值方法来快速检测太阳冠空洞，通过图像处理和形态学运算从分割图像中提取冠空洞区域，与现有技术相比，在较短时间内展现了可比较的性能。

一种基于量子模糊的实时太阳冕孔检测方法

A Quantum Fuzzy-based Approach for Real-Time Detection of Solar Coronal  Holes

Automated X-ray image segmentation would accelerate research and development
in diagnostic and interventional precision medicine. Prior efforts have
contributed task-specific models capable of solving specific image analysis
problems, but the utility of these models is restricted to their particular
task domain, and expanding to broader use requires additional data, labels, and
retraining efforts. Recently, foundation models (FMs) -- machine learning
models trained on large amounts of highly variable data thus enabling broad
applicability -- have emerged as promising tools for automated image analysis.
Existing FMs for medical image analysis focus on scenarios and modalities where
objects are clearly defined by visually apparent boundaries, such as surgical
tool segmentation in endoscopy. X-ray imaging, by contrast, does not generally
offer such clearly delineated boundaries or structure priors. During X-ray
image formation, complex 3D structures are projected in transmission onto the
imaging plane, resulting in overlapping features of varying opacity and shape.
To pave the way toward an FM for comprehensive and automated analysis of
arbitrary medical X-ray images, we develop FluoroSAM, a language-aligned
variant of the Segment-Anything Model, trained from scratch on 1.6M synthetic
X-ray images. FluoroSAM is trained on data including masks for 128 organ types
and 464 non-anatomical objects, such as tools and implants. In real X-ray
images of cadaveric specimens, FluoroSAM is able to segment bony anatomical
structures based on text-only prompting with 0.51 and 0.79 DICE with
point-based refinement, outperforming competing SAM variants for all
structures. FluoroSAM is also capable of zero-shot generalization to segmenting
classes beyond the training set thanks to its language alignment, which we
demonstrate for full lung segmentation on real chest X-rays.

通过 FluoroSAM 模型，该研究针对医学 X 光图像，基于文本提示，在解剖结构分割方面表现出色，并且具有零样本泛化能力，能够扩展到未经训练的图像类别，从而推进医学诊断和干预的自动化分析。